{"title":"生物启发的特征集降维优化","authors":"Esraa Elhariri, Nashwa El-Bendary, A. Hassanien","doi":"10.1109/ACTEA.2016.7560136","DOIUrl":null,"url":null,"abstract":"In this paper, two novel bio-inspired optimization algorithms; namely Dragonfly Algorithm (DA) and Grey Wolf Optimizer (GWO), have been applied for fulfilling the goal of feature set dimensional reduction. The proposed classification system has been tested via solving the problem of Electromyography (EMG) signal classification with optimal features subset selection. The obtained experimental results showed that the GWO based Support Vector Machines (SVM) classification algorithm has achieved an accuracy of 93.22% using 31% of the total extracted features. It also outperformed both the typical SVM algorithm, with no feature set optimization, and the DA based optimized feature set SVM classification, for the tested EMG dataset.","PeriodicalId":220936,"journal":{"name":"2016 3rd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Bio-inspired optimization for feature set dimensionality reduction\",\"authors\":\"Esraa Elhariri, Nashwa El-Bendary, A. Hassanien\",\"doi\":\"10.1109/ACTEA.2016.7560136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, two novel bio-inspired optimization algorithms; namely Dragonfly Algorithm (DA) and Grey Wolf Optimizer (GWO), have been applied for fulfilling the goal of feature set dimensional reduction. The proposed classification system has been tested via solving the problem of Electromyography (EMG) signal classification with optimal features subset selection. The obtained experimental results showed that the GWO based Support Vector Machines (SVM) classification algorithm has achieved an accuracy of 93.22% using 31% of the total extracted features. It also outperformed both the typical SVM algorithm, with no feature set optimization, and the DA based optimized feature set SVM classification, for the tested EMG dataset.\",\"PeriodicalId\":220936,\"journal\":{\"name\":\"2016 3rd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 3rd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACTEA.2016.7560136\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 3rd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACTEA.2016.7560136","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
摘要
本文提出了两种新颖的仿生优化算法;分别采用蜻蜓算法(Dragonfly Algorithm, DA)和灰狼优化器(Grey Wolf Optimizer, GWO)来实现特征集降维的目标。通过解决肌电信号的最优特征子集选择问题,对所提出的分类系统进行了验证。实验结果表明,基于GWO的支持向量机(SVM)分类算法在提取总特征的31%的情况下,准确率达到93.22%。对于已测试的肌电图数据集,它的性能也优于未进行特征集优化的典型SVM算法和基于DA的优化特征集SVM分类。
Bio-inspired optimization for feature set dimensionality reduction
In this paper, two novel bio-inspired optimization algorithms; namely Dragonfly Algorithm (DA) and Grey Wolf Optimizer (GWO), have been applied for fulfilling the goal of feature set dimensional reduction. The proposed classification system has been tested via solving the problem of Electromyography (EMG) signal classification with optimal features subset selection. The obtained experimental results showed that the GWO based Support Vector Machines (SVM) classification algorithm has achieved an accuracy of 93.22% using 31% of the total extracted features. It also outperformed both the typical SVM algorithm, with no feature set optimization, and the DA based optimized feature set SVM classification, for the tested EMG dataset.