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GSLDWOA: A Feature Selection Algorithm for Intrusion Detection Systems in IIoT 认领 引用
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作者 Wanwei Huang Huicong Yu +3 位作者 Jiawei Ren Kun Wang Yanbu Guo Lifeng Jin 《Computers, Materials & Continua》 SCIE EI 2026年第1期2006-2029,共24页
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from... Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%. 展开更多
关键词 Industrial Internet of Things intrusion detection system feature selection whale optimization algorithm Gaussian mutation
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A Novel Hybrid Sine Cosine-Flower Pollination Algorithm for Optimized Feature Selection 认领 引用
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作者 Sumbul Azeem Shazia Javed +3 位作者 Farheen Ibraheem Uzma Bashir Nazar Waheed Khursheed Aurangzeb 《Computers, Materials & Continua》 SCIE EI 2026年第5期1916-1930,共15页
Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset t... Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset to another.Only the relevant features contributemeaningfully to classificationaccuracy.The presence of irrelevant features reduces the system’s effectiveness.Classification performance often deteriorates on high-dimensional datasets due to the large search space.Thus,one of the significant obstacles affecting the performance of the learning process in the majority of machine learning and data mining techniques is the dimensionality of the datasets.Feature selection(FS)is an effective preprocessing step in classification tasks.The aim of applying FS is to exclude redundant and unrelated features while retaining the most informative ones to optimize classification capability and compress computational complexity.In this paper,a novel hybrid binary metaheuristic algorithm,termed hSC-FPA,is proposed by hybridizing the Flower Pollination Algorithm(FPA)and the Sine Cosine Algorithm(SCA).Hybridization controls the exploration capacity of SCA and the exploitation behavior of FPA to maintain a balanced search process.SCA guides the global search in the early iterations,while FPA’s local pollination refines promising solutions in later stages.A binary conversion mechanism using a threshold function is implemented to handle the discrete nature of the feature selection problem.The functionality of the proposed hSC-FPA is authenticated on fourteen standard datasets from the UCI repository using the K-Nearest Neighbors(K-NN)classifier.Experimental results are benchmarked against the standalone SCA and FPA algorithms.The hSC-FPA consistently achieves higher classification accuracy,selects a more compact feature subset,and demonstrates superior convergence behavior.These findings support the stability and outperformance of the hybrid feature selection method presented. 展开更多
关键词 Classification algorithms feature selection process flower pollination algorithm hybrid model metaheuristics multi-objective optimization search algorithm sine cosine algorithm
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Lightweight Airborne Vision Abnormal Behavior Detection Algorithm Based on Dual-Path Feature Optimization 认领 引用
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作者 Baixuan Han Yueping Peng +5 位作者 Zecong Ye Hexiang Hao Xuekai Zhang Wei Tang Wenchao Kang Qilong Li 《Computers, Materials & Continua》 SCIE EI 2026年第2期754-784,共31页
Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on ... Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed.By integrating multi-head grouped self-attention mechanism and Partial-Conv,a two-way feature grouping fusion module(DFPF)was designed,which carried out effective channel segmentation and fusion strategies to reduce redundant calculations andmemory access.C3K2 module was improved,and then unstructured pruning and feature distillation technologywere used.The algorithmmodel is lightweight,and the feature extraction ability for airborne visual abnormal behavior targets is strengthened,and the computational efficiency of the model is improved.Finally,we test the generalization of the baseline model and the improved model on the VisDrone2019 dataset.The results show that com-pared with the baseline model,the detection accuracy of the final improved model on the airborne visual abnormal behavior dataset is improved from 90.2% to 94.8%,and the model parameters are reduced by 50.9% to meet the detection requirements of high efficiency and high precision.The detection accuracy of the improved model on the Vis-Drone2019 public dataset is 1.3% higher than that of the baseline model,indicating the effectiveness of the improved method in this paper. 展开更多
关键词 YOLOv11 algorithm multi-class abnormal behavior detection feature extraction UAV aerial photography datasets
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Optimizing IoT-Driven Smart Cities with the Dynamic Leader Sibha Algorithm:A Novel Approach to Feature Selection and Hyperparameter Tuning 认领 引用
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作者 Safaa Zaman Marwa M.Eid +2 位作者 Ebrahim A.Mattar Doaa Sami Khafaga El-Sayed M.El-Kenawy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期899-958,共60页
The rapid growth of Internet of Things(IoT)technologies has transformed modern urban environments into complex smart cities,generating vast amounts of high-dimensional,heterogeneous data.Effectively analyzing this dat... The rapid growth of Internet of Things(IoT)technologies has transformed modern urban environments into complex smart cities,generating vast amounts of high-dimensional,heterogeneous data.Effectively analyzing this data is crucial for optimizing urban infrastructure,enhancing quality of life,and supporting sustainable development.However,smart city data presents significant challenges,including non-linear dependencies,noisy signals,and high dimensionality.To address these challenges,this study proposes the Dynamic Leader Sibha Algorithm(DLSA),a novel metaheuristic optimization technique inspired by the structured counting dynamics of the Sibha.The DLSA was applied to the Smart Cities Index dataset,leveraging copula functions to model complex,multivariate dependencies and enhance predictive accuracy.The baseline machine learning(ML)evaluation revealed that the ExtraTreesRegressor achieved the lowest mean squared error(MSE)of 0.007462409,highlighting its superior initial performance.Following feature selection using the binary Dynamic Leader Sibha Algorithm(bSiba),the average error was reduced to 0.373245769,significantly improving data quality and model efficiency.Subsequent ML evaluation after feature selection further reduced the MSE of the ExtraTreesRegressor to 0.00151927,reflecting the effectiveness of dimensionality reduction.Finally,hyperparameter optimization using the DLSA achieved a remarkable MSE of 1.32249×10−6 with the Siba+ExtraTreesRegressor combination,demonstrating the algorithm’s powerful optimization capabilities.These findings indicate that the DLSA framework can significantly enhance the predictive performance of IoT-driven smart city models,offering valuable insights for urban planners,policymakers,and technology developers seeking to build smarter,more resilient cities. 展开更多
关键词 Smart cities analytics Internet of Things(IoT) Dynamic Leader Sibha Algorithm(DLSA) feature selection and optimization high-dimensional data analysis
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Enhancing Epileptic Seizure Classification via Multi-Feature Fusion in a Transformer-LSTM Architecture 认领 引用
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作者 Gaoteng Yuan Ping Qiu +3 位作者 Qika Lin Jianchu Lin Xiang Li Dongping Gao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1134-1154,共21页
Epilepsy is a chronic neurological disorder characterized by recurrent seizures,posing significant challenges to patients’quality of life.Accurate classification of seizure states is crucial for effective interventio... Epilepsy is a chronic neurological disorder characterized by recurrent seizures,posing significant challenges to patients’quality of life.Accurate classification of seizure states is crucial for effective intervention.This paper presents a deep learning-based approach for epileptic seizure classification by integrating multi-feature analysis of electroencephalogram(EEG)signals.The proposed method begins with signal preprocessing,including denoising,segmentation,and label construction.Subsequently,a comprehensive set of temporal,spectral,and wavelet-based features—such as signal mean,power,heart rate,and wavelet coefficients—is extracted.Feature selection is then performed using the Maximal Information Coefficient(MIC)to identify the most discriminative inputs.A hybrid model combining a Transformer encoder and a Long Short-Term Memory(LSTM)network is developed to effectively capture both long-range dependencies and temporal dynamics in EEG sequences for seizure classification.Evaluated on the Bonn dataset using 5-fold cross-validation,the proposed method achieves an accuracy of 96.43%in distinguishing between epileptic patients and healthy subjects,with a sensitivity of 97.53%in detecting seizure states.It also attains a multi-class classification accuracy of 90.14%across different epileptic signal types.Ablation studies confirm that MICbased feature selection improves accuracy by over 2O%compared to using raw features without selection.The results demonstrate that the integration of multi-feature analysis with the Transformer-LSTM architecture offers an effective and reliable solution for EEG-based seizure classification. 展开更多
关键词 Epileptic classification EEG signal wavelet analysis feature selection transformer-LSTM Algorithm 2:MIC algorithm feature selection
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Federated Multi-Label Feature Selection via Dual-Layer Hybrid Breeding Cooperative Particle Swarm Optimization with Manifold and Sparsity Regularization 认领 引用
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作者 Songsong Zhang Huazhong Jin +5 位作者 Zhiwei Ye Jia Yang Jixin Zhang Dongfang Wu Xiao Zheng Dingfeng Song 《Computers, Materials & Continua》 SCIE EI 2026年第1期1141-1159,共19页
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal... Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics. 展开更多
关键词 Multi-label feature selection federated learning manifold regularization sparse constraints hybrid breeding optimization algorithm particle swarm optimizatio algorithm privacy protection
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A Feature Selection Method for Software Defect Prediction Based on Improved Beluga Whale Optimization Algorithm 认领 引用 被引量：1
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作者 Shaoming Qiu Jingjie He +1 位作者 Yan Wang Bicong E 《Computers, Materials & Continua》 SCIE EI 2025年第6期4879-4898,共20页
Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software ... Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software defect prediction can be effectively performed using traditional features,but there are some redundant or irrelevant features in them(the presence or absence of this feature has little effect on the prediction results).These problems can be solved using feature selection.However,existing feature selection methods have shortcomings such as insignificant dimensionality reduction effect and low classification accuracy of the selected optimal feature subset.In order to reduce the impact of these shortcomings,this paper proposes a new feature selection method Cubic TraverseMa Beluga whale optimization algorithm(CTMBWO)based on the improved Beluga whale optimization algorithm(BWO).The goal of this study is to determine how well the CTMBWO can extract the features that are most important for correctly predicting software defects,improve the accuracy of fault prediction,reduce the number of the selected feature and mitigate the risk of overfitting,thereby achieving more efficient resource utilization and better distribution of test workload.The CTMBWO comprises three main stages:preprocessing the dataset,selecting relevant features,and evaluating the classification performance of the model.The novel feature selection method can effectively improve the performance of SDP.This study performs experiments on two software defect datasets(PROMISE,NASA)and shows the method’s classification performance using four detailed evaluation metrics,Accuracy,F1-score,MCC,AUC and Recall.The results indicate that the approach presented in this paper achieves outstanding classification performance on both datasets and has significant improvement over the baseline models. 展开更多
关键词 Software defect prediction feature selection beluga optimization algorithm triangular wandering strategy cauchy mutation reverse learning
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A novel fusion of interpretable boosting algorithm and feature selection for predicting casing damage 认领 引用
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作者 Juan Li Mandella Ali M.Fargalla +7 位作者 Wei Yan Zi-Xu Zhang Wei Zhang Zi-Chen Zou Tang Qing Tao Yang Chao-Dong Tan Guang-Cong Li 《Petroleum Science》 SCIE EI CAS CSCD 2025年第10期4157-4173,共17页
Casing damage resulting from sand production in unconsolidated sandstone reservoirs can significantly impact the average production of oil wells.However,the prediction task remains challenging due to the complex damag... Casing damage resulting from sand production in unconsolidated sandstone reservoirs can significantly impact the average production of oil wells.However,the prediction task remains challenging due to the complex damage mechanism caused by sand production.This paper presents an innovative approach that combines feature selection(FS)with boosting algorithms to accurately predict casing damage in unconsolidated sandstone reservoirs.A novel TriScore FS technique is developed,combining mRMR,Random Forest,and F-test.The approach integrates three distinct feature selection approaches—TriScore,wrapper,and hybrid TriScore-wrapper and four interpretable Boosting models(AdaBoost,XGBoost,LightGBM,CatBoost).Moreover,shapley additive explanations(SHAP)was used to identify the most significant features across engineering,geological,and production features.The CatBoost model,using the Hybrid TriScore-rapper G1G2FS method,showed exceptional performance in analyzing data from the Gangxi Oilfield.It achieved the highestaccuracy(95.5%)and recall rate(89.7%)compared to other tested models.Casing service time,casing wall thickness,and perforation density were selected as the top three most important features.This framework enhances predictive robustness and is an effective tool for policymakers and energy analysts,confirming its capability to deliver reliable casing damage forecasts. 展开更多
关键词 Casing damage Machine learning Feature selection Sand production Boosting algorithm
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Particle Swarm Optimization Algorithm for Feature Selection Inspired by Peak Ecosystem Dynamics 认领 引用
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作者 Shaobo Deng Meiru Xie +3 位作者 Bo Wang Shuaikun Zhang Sujie Guan Min Li 《Computers, Materials & Continua》 SCIE EI 2025年第2期2723-2751,共29页
In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update ... In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update based on two extreme values: personal best and global best, which limits the diversity of information. Ideally, particles should learn from multiple advantageous particles to enhance interactivity and optimization efficiency. Accordingly, this paper proposes a PSO that simulates the evolutionary dynamics of species survival in mountain peak ecology (PEPSO) for feature selection. Based on the pyramid topology, the algorithm simulates the features of mountain peak ecology in nature and the competitive-cooperative strategies among species. According to the principles of the algorithm, the population is first adaptively divided into many subgroups based on the fitness level of particles. Then, particles within each subgroup are divided into three different types based on their evolutionary levels, employing different adaptive inertia weight rules and dynamic learning mechanisms to define distinct learning modes. Consequently, all particles play their respective roles in promoting the global optimization performance of the algorithm, similar to different species in the ecological pattern of mountain peaks. Experimental validation of the PEPSO performance was conducted on 18 public datasets. The experimental results demonstrate that the PEPSO outperforms other PSO variant-based feature selection methods and mainstream feature selection methods based on intelligent optimization algorithms in terms of overall performance in global search capability, classification accuracy, and reduction of feature space dimensions. Wilcoxon signed-rank test also confirms the excellent performance of the PEPSO. 展开更多
关键词 Machine learning feature selection evolutionary algorithm particle swarm optimization
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Research on Vehicle Safety Based on Multi-Sensor Feature Fusion for Autonomous Driving Task 认领 引用
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作者 Yang Su Xianrang Shi Tinglun Song 《Computers, Materials & Continua》 SCIE EI 2025年第6期5831-5848,共18页
Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhan... Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving.This study aims to enhance the learning efficiency ofmulti-sensor feature fusion in autonomous driving tasks,thereby improving the safety and responsiveness of the system.To achieve this goal,we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities:visual,radar,and lidar data.The model optimizes the feature fusion process through the introduction of two novel mechanisms:Sparse Channel Pooling(SCP)and Residual Triplet-Attention(RTA).Firstly,the SCP mechanism enables the model to adaptively filter out salient feature channels while eliminating the interference of redundant features.This enhances the model’s emphasis on critical features essential for decisionmaking and strengthens its robustness to environmental variability.Secondly,the RTA mechanism addresses the issue of feature misalignment across different modalities by effectively aligning key cross-modal features.This alignment reduces the computational overhead associated with redundant features and enhances the overall efficiency of the system.Furthermore,this study incorporates a reinforcement learning module designed to optimize strategies within a continuous action space.By integrating thismodulewith the feature fusion learning process,the entire system is capable of learning efficient driving strategies in an end-to-end manner within the CARLA autonomous driving simulator.Experimental results demonstrate that the proposedmodel significantly enhances the perception and decision-making accuracy of the autonomous driving system in complex traffic scenarios while maintaining real-time responsiveness.This work provides a novel perspective and technical pathway for the application of multi-sensor data fusion in autonomous driving. 展开更多
关键词 Multi-sensor fusion autonomous driving feature selection attention mechanism reinforcement learning
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AI-Integrated Feature Selection of Intrusion Detection for Both SDN and Traditional Network Architectures Using an Improved Crayfish Optimization Algorithm 认领 引用
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作者 Hui Xu Wei Huang Longtan Bai 《Computers, Materials & Continua》 SCIE EI 2025年第8期3053-3073,共21页
With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with ... With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with complex attacks in SDN environments,thus to address the network security issues from the viewpoint of Artificial Intelligence(AI),this paper introduces the Crayfish Optimization Algorithm(COA)to the field of intrusion detection for both SDN and traditional network architectures,and based on the characteristics of the original COA,an Improved Crayfish Optimization Algorithm(ICOA)is proposed by integrating strategies of elite reverse learning,Levy flight,crowding factor and parameter modification.The ICOA is then utilized for AI-integrated feature selection of intrusion detection for both SDN and traditional network architectures,to reduce the dimensionality of the data and improve the performance of network intrusion detection.Finally,the performance evaluation is performed by testing not only the NSL-KDD dataset and the UNSW-NB 15 dataset for traditional networks but also the InSDN dataset for SDN-based networks.Experimental results show that ICOA improves the accuracy by 0.532%and 2.928%respectively compared with GWO and COA in traditional networks.In SDN networks,the accuracy of ICOA is 0.25%and 0.3%higher than COA and PSO.These findings collectively indicate that AI-integrated feature selection based on the proposed ICOA can promote network intrusion detection for both SDN and traditional architectures. 展开更多
关键词 Software-defined networking(SDN) intrusion detection artificial intelligence(AI) feature selection crayfish optimization algorithm(COA)
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An Intelligent Multi-Stage GA–SVM Hybrid Optimization Framework for Feature Engineering and Intrusion Detection in Internet of Things Networks 认领 引用
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作者 Isam Bahaa Aldallal Abdullahi Abdu Ibrahim Saadaldeen Rashid Ahmed 《Computers, Materials & Continua》 SCIE EI 2026年第4期985-1007,共23页
The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT n... The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches. 展开更多
关键词 Cybersecurity intrusion detection system(IDS) IoT support vector machines(SVM) genetic algorithms(GA) feature selection NSL-KDD dataset anomaly detection
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Feature Selection Optimisation for Cancer Classification Based on Evolutionary Algorithms:An Extensive Review 认领 引用
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作者 Siti Ramadhani Lestari Handayani +4 位作者 Theam Foo Ng Sumayyah Dzulkifly Roziana Ariffin Haldi Budiman Shir Li Wang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第6期2711-2765,共55页
In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classificati... In recent years,feature selection(FS)optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification.This work reviews FS and classification methods that utilize evolutionary algorithms(EAs)for gene expression profiles in cancer or medical applications based on research motivations,challenges,and recommendations.Relevant studies were retrieved from four major academic databases-IEEE,Scopus,Springer,and ScienceDirect-using the keywords‘cancer classification’,‘optimization’,‘FS’,and‘gene expression profile’.A total of 67 papers were finally selected with key advancements identified as follows:(1)The majority of papers(44.8%)focused on developing algorithms and models for FS and classification.(2)The second category encompassed studies on biomarker identification by EAs,including 20 papers(30%).(3)The third category comprised works that applied FS to cancer data for decision support system purposes,addressing high-dimensional data and the formulation of chromosome length.These studies accounted for 12%of the total number of studies.(4)The remaining three papers(4.5%)were reviews and surveys focusing on models and developments in prediction and classification optimization for cancer classification under current technical conditions.This review highlights the importance of optimizing FS in EAs to manage high-dimensional data effectively.Despite recent advancements,significant limitations remain:the dynamic formulation of chromosome length remains an underexplored area.Thus,further research is needed on dynamic-length chromosome techniques for more sophisticated biomarker gene selection techniques.The findings suggest that further advancements in dynamic chromosome length formulations and adaptive algorithms could enhance cancer classification accuracy and efficiency. 展开更多
关键词 Feature selection(FS) gene expression profile(GEP) cancer classification evolutionary algorithms(EAs) dynamic-length chromosome
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Hydraulic directional valve fault diagnosis using a weighted adaptive fusion of multi-dimensional features of a multi-sensor 认领 引用 被引量：22
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作者 Jin-chuan SHI Yan REN +1 位作者 He-sheng TANG Jia-wei XIANG 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2022年第4期257-271,共15页
Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise,the traditional single sensor monitoring technology is difficult to use for an accurate diagnos... Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise,the traditional single sensor monitoring technology is difficult to use for an accurate diagnosis of it.Therefore,a fault diagnosis method based on multi-sensor information fusion is proposed in this paper to reduce the inaccuracy and uncertainty of traditional single sensor information diagnosis technology and to realize accurate monitoring for the location or diagnosis of early faults in such valves in noisy environments.Firstly,the statistical features of signals collected by the multi-sensor are extracted and the depth features are obtained by a convolutional neural network(CNN)to form a complete and stable multi-dimensional feature set.Secondly,to obtain a weighted multi-dimensional feature set,the multi-dimensional feature sets of similar sensors are combined,and the entropy weight method is used to weight these features to reduce the interference of insensitive features.Finally,the attention mechanism is introduced to improve the dual-channel CNN,which is used to adaptively fuse the weighted multi-dimensional feature sets of heterogeneous sensors,to flexibly select heterogeneous sensor information so as to achieve an accurate diagnosis.Experimental results show that the weighted multi-dimensional feature set obtained by the proposed method has a high fault-representation ability and low information redundancy.It can diagnose simultaneously internal wear faults of the hydraulic directional valve and electromagnetic faults of actuators that are difficult to diagnose by traditional methods.This proposed method can achieve high fault-diagnosis accuracy under severe working conditions. 展开更多
关键词 Hydraulic directional valve Internal fault diagnosis Weighted multi-dimensional features Multi-sensor information fusion
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Well production optimization using streamline features-based objective function and Bayesian adaptive direct search algorithm 认领 引用 被引量：10
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作者 Qi-Hong Feng Shan-Shan Li +2 位作者 Xian-Min Zhang Xiao-Fei Gao Ji-Hui Ni 《Petroleum Science》 SCIE CAS CSCD 2022年第6期2879-2894,共16页
Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.T... Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development. 展开更多
关键词 Well production Optimization efficiency Streamline simulation Streamline feature Objective function Bayesian adaptive direct search algorithm
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A Length-Adaptive Non-Dominated Sorting Genetic Algorithm for Bi-Objective High-Dimensional Feature Selection 认领 引用 被引量：7
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作者 Yanlu Gong Junhai Zhou +2 位作者 Quanwang Wu MengChu Zhou Junhao Wen 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第9期1834-1844,共11页
As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected featu... As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected features.Evolutionary computing(EC)is promising for FS owing to its powerful search capability.However,in traditional EC-based methods,feature subsets are represented via a length-fixed individual encoding.It is ineffective for high-dimensional data,because it results in a huge search space and prohibitive training time.This work proposes a length-adaptive non-dominated sorting genetic algorithm(LA-NSGA)with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective highdimensional FS.In LA-NSGA,an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths,and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space adaptively.Moreover,a dominance-based local search method is employed for further improvement.The experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms. 展开更多
关键词 Bi-objective optimization feature selection(FS) genetic algorithm high-dimensional data length-adaptive
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Dynamic Individual Selection and Crossover Boosted Forensic-based Investigation Algorithm for Global Optimization and Feature Selection 认领 引用 被引量：4
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作者 Hanyu Hu Weifeng Shan +5 位作者 Jun Chen Lili Xing Ali Asghar Heidari Huiling Chen Xinxin He Maofa Wang 《Journal of Bionic Engineering》 SCIE EI CSCD 2023年第5期2416-2442,共27页
The advent of Big Data has rendered Machine Learning tasks more intricate as they frequently involve higher-dimensional data.Feature Selection(FS)methods can abate the complexity of the data and enhance the accuracy,g... The advent of Big Data has rendered Machine Learning tasks more intricate as they frequently involve higher-dimensional data.Feature Selection(FS)methods can abate the complexity of the data and enhance the accuracy,generalizability,and interpretability of models.Meta-heuristic algorithms are often utilized for FS tasks due to their low requirements and efficient performance.This paper introduces an augmented Forensic-Based Investigation algorithm(DCFBI)that incorporates a Dynamic Individual Selection(DIS)and crisscross(CC)mechanism to improve the pursuit phase of the FBI.Moreover,a binary version of DCFBI(BDCFBI)is applied to FS.Experiments conducted on IEEE CEC 2017 with other metaheuristics demonstrate that DCFBI surpasses them in search capability.The influence of different mechanisms on the original FBI is analyzed on benchmark functions,while its scalability is verified by comparing it with the original FBI on benchmarks with varied dimensions.BDCFBI is then applied to 18 real datasets from the UCI machine learning database and the Wieslaw dataset to select near-optimal features,which are then compared with six renowned binary metaheuristics.The results show that BDCFBI can be more competitive than similar methods and acquire a subset of features with superior classification accuracy. 展开更多
关键词 Feature selection Forensic-based investigation algorithm Crisscross mechanism Global optimization Metaheuristic algorithms Bionic algorithm
A multi-criteria fusion feature selection algorithm for fault diagnosis of helicopter planetary gear train 认领 引用 被引量：9
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作者 Canfei SUN Youren WANG Guodong SUN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2020年第5期1549-1561,共13页
Planetary gear train is a prominent component of helicopter transmission system and its health is of great significance for the flight safety of the helicopter.During health condition monitoring,the selection of a fau... Planetary gear train is a prominent component of helicopter transmission system and its health is of great significance for the flight safety of the helicopter.During health condition monitoring,the selection of a fault sensitive feature subset is meaningful for fault diagnosis of helicopter planetary gear train.According to actual situation,this paper proposed a multi-criteria fusion feature selection algorithm (MCFFSA) to identify an optimal feature subset from the highdimensional original feature space.In MCFFSA,a fault feature set of multiple domains,including time domain,frequency domain and wavelet domain,is first extracted from the raw vibration dataset.Four targeted criteria are then fused by multi-objective evolutionary algorithm based on decomposition (MOEA/D) to find Proto-efficient subsets,wherein two criteria for measuring diagnostic performance are assessed by sparse Bayesian extreme learning machine (SBELM).Further,Fmeasure is adopted to identify the optimal feature subset,which was employed for subsequent fault diagnosis.The effectiveness of MCFFSA is validated through six fault recognition datasets from a real helicopter transmission platform.The experimental results illustrate the superiority of combination of MOEA/D and SBELM in MCFFSA,and comparative analysis demonstrates that the optimal feature subset provided by MCFFSA can achieve a better diagnosis performance than other algorithms. 展开更多
关键词 Fault detection Feature selection F-measure Helicopter planetary gear train Multi-objective evolutionary algorithm
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Age estimation from facial images based on Gabor feature fusion and the CIASO-SA algorithm 认领 引用 被引量：3
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作者 Di Lu Dapeng Wang +1 位作者 Kaiyu Zhang Xiangyuan Zeng 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第2期518-531,共14页
Aiming at the problem of long time-consuming and low accuracy of existing age estimation approaches,a new age estimation method using Gabor feature fusion,and an improved atomic search algorithm for feature selection ... Aiming at the problem of long time-consuming and low accuracy of existing age estimation approaches,a new age estimation method using Gabor feature fusion,and an improved atomic search algorithm for feature selection is proposed.Firstly,texture features of five scales and eight directions in the face region are extracted by Gabor wavelet transform.The statistical histogram is introduced to encode and fuse the directional index with the largest feature value on Gabor scales.Secondly,a new hybrid feature selection algorithm chaotic improved atom search optimisation with simulated annealing(CIASO-SA)is presented,which is based on an improved atomic search algorithm and the simulated annealing algorithm.Besides,the CIASO-SA algorithm introduces a chaos mechanism during atomic initialisation,significantly improving the convergence speed and accuracy of the algorithm.Finally,a support vector machine(SVM)is used to get classification results of the age group.To verify the performance of the proposed algorithm,face images with three resolutions in the Adience dataset are tested.Using the Gabor real part fusion feature at 48�48 resolution,the average accuracy and 1-off accuracy of age classification exhibit a maximum of 60.4%and 85.9%,respectively.Obtained results prove the superiority of the proposed algorithm over the state-of-the-art methods,which is of great referential value for application to the mobile terminals. 展开更多
关键词 age estimation atom search algorithm feature selection Gabor feature simulated annealing
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Spectral matching algorithm based on nonsubsampled contourlet transform and scale-invariant feature transform 认领 引用 被引量：4
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作者 Dong Liang Pu Yan +2 位作者 Ming Zhu Yizheng Fan Kui Wang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第3期453-459,共7页
A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low freq... A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy. 展开更多
关键词 point pattern matching nonsubsampled contourlet transform scale-invariant feature transform spectral algorithm.
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