{"title":"特征选择算法在人体活动分类中的性能评价","authors":"Gokalp Tulum, N. T. Artug, B. Bolat","doi":"10.1109/INISTA.2013.6577634","DOIUrl":null,"url":null,"abstract":"In this work, four human activities were classified by using multi layer perceptron and k-nearest neighbours algorithm. Due to mass amount of data, two different feature selection methods, which are ReliefF and t-score, were applied to the data. The best result is obtained as 97.6% with 51 features selected by ReliefF.","PeriodicalId":301458,"journal":{"name":"2013 IEEE INISTA","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Performance evaluation of feature selection algorithms on human activity classification\",\"authors\":\"Gokalp Tulum, N. T. Artug, B. Bolat\",\"doi\":\"10.1109/INISTA.2013.6577634\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, four human activities were classified by using multi layer perceptron and k-nearest neighbours algorithm. Due to mass amount of data, two different feature selection methods, which are ReliefF and t-score, were applied to the data. The best result is obtained as 97.6% with 51 features selected by ReliefF.\",\"PeriodicalId\":301458,\"journal\":{\"name\":\"2013 IEEE INISTA\",\"volume\":\"103 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE INISTA\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INISTA.2013.6577634\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE INISTA","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INISTA.2013.6577634","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Performance evaluation of feature selection algorithms on human activity classification
In this work, four human activities were classified by using multi layer perceptron and k-nearest neighbours algorithm. Due to mass amount of data, two different feature selection methods, which are ReliefF and t-score, were applied to the data. The best result is obtained as 97.6% with 51 features selected by ReliefF.
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