DEPSOSVM:variant of differential evolution based on PSO for image and text data classification

作     者：Abhishek Dixit Ashish Mani Rohit Bansal 

作者机构：Department of Computer ScienceAmity University Uttar PradeshNoidaIndia Department of EEEAmity University Uttar PradeshNoidaIndia Department of Management StudiesRajiv Gandhi Institute of Petroleum TechnologyRae BareliIndia 

出 版 物：《International Journal of Intelligent Computing and Cybernetics》 (智能计算与控制论国际期刊（英文）)

年 卷 期：2020年第13卷第2期

页      面：223-238页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Support vector machine(SVM) Differential evolution(DE) Particle swarm optimization(PSO)and Global optimization 

摘      要：Purpose-Feature selection is an important step for data pre-processing specially in the case of high dimensional data *** of the data model is reduced if the model is trained with high dimensional data set,and it results in poor classification ***,before training the model an important step to apply is the feature selection on the dataset to improve the performance and classification ***/methodology/approach-A novel optimization approach that hybridizes binary particle swarm optimization(BPSO)and differential evolution(DE)for fine tuning of SVM classifier is *** name of the implemented classifier is given as ***-This approach is evaluated using 20 UCI benchmark text data classification data ***,the performance of the proposed technique is also evaluated on UCI benchmark image data set of cancer *** the results,it can be observed that the proposed DEPSOSVMtechniques have significant improvement in performance over other algorithms in the literature for feature *** proposed technique shows better classification accuracy as ***/value-The proposed approach is different from the previous work,as in all the previous work DE/(rand/1)mutation strategy is used whereas in this study DE/(rand/2)is used and the mutation strategy with BPSO is *** difference is on the crossover approach in our case as we have used a novel approach of comparing best particle with sigmoid *** core contribution of this paper is to hybridize DE with BPSO combined with SVM classifier(DEPSOSVM)to handle the feature selection problems.