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A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification

Computers, Materials & Continua 2023年第3期74卷 5799-5820页

作者： Noureen Talpur Said Jadid Abdulkadir Mohd Hilmi Hasan Hitham Alhussian ayed alwadain Computer and Information Sciences Department Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Computer Science Department Community CollegeKing Saud UniversityRiyadh145111Saudi Arabia

Machine learning(ML)practices such as classification have played a very important role in classifying diseases in medical *** medical science is a sensitive field,the pre-processing of medical data requires careful handling to make quality clinical ***,medical data is considered high-dimensional and complex data that contains many irrelevant and redundant *** factors indirectly upset the disease prediction and classification accuracy of any ML *** address this issue,various data pre-processing methods called Feature Selection(FS)techniques have been presented in the ***,the majority of such techniques frequently suffer from local minima issues due to large solution ***,this study has proposed a novel wrapper-based Sand Cat SwarmOptimization(SCSO)technique as an FS approach to find optimum features from ten benchmark medical *** SCSO algorithm replicates the hunting and searching strategies of the sand cat while having the advantage of avoiding local optima and finding the ideal solution with minimal control ***,K-Nearest Neighbor(KNN)classifier was used to evaluate the effectiveness of the features identified by the proposed SCSO *** performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy,optimum feature size,and computational cost in *** simulation results on the benchmark medical datasets revealed that the proposed SCSO-KNN approach has outperformed comparative algorithms with an average classification accuracy of 93.96%by selecting 14.2 features within 1.91 ***,the Wilcoxon rank test was used to perform the significance analysis between the proposed SCSOKNN method and six other algorithms for a p-value less than *** findings revealed that the proposed algorithm produces better outcomes with an average p-value o

关键词： Machine learning optimization feature selection classification medical data

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Enhanced Multi-Objective Grey Wolf Optimizer with Lévy Flight and Mutation Operators for Feature Selection

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Computer Systems Science & Engineering 2023年第11期47卷 1937-1966页

作者： Qasem Al-Tashi Tareq M Shami Said Jadid Abdulkadir Emelia Akashah Patah Akhir ayed alwadain Hitham Alhussain Alawi Alqushaibi Helmi MD Rais Amgad Muneer Maliazurina B.Saad Jia Wu Seyedali Mirjalili Department of Imaging Physics The University of Texas MD Anderson Cancer CenterHoustonTXUSA Department of Electronic Engineering University of YorkYorkYO105DDUK Computer and Information Sciences Department Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Computer Science Department Community CollegeKing Saud UniversityRiyadhP.O.Box 145111Saudi Arabia Torrens University Australia BrisbaneQLDAustralia Yonsei Frontier Lab Yonsei UniversitySeoulKorea University Research and Innovation Center Obuda UniversityBudapest1034Hungary

The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be *** it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization *** paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection *** proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation *** Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected *** mutation operator is integrated to add more informative features that can assist in enhancing classification *** feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid *** evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization ***,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost.

关键词： Feature selection multi-objective optimization grey wolf optimizer Lévy flight mutation classification

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