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In this study, we proposed a new FS method named as Extensive Feature Selector (EFS), which benefits from corpus-based and class-based probabilities in its calculations. The performance of EFS is compared with nine well-known FS methods, namely, Chi-Squared (CHI2), Class Discriminating Measure (CDM), Discriminative Power Measure (DPM), Odds Ratio (OR), Distinguishing Feature Selector (DFS), Comprehensively Measure Feature Selection (CMFS), Discriminative Feature Selection (DFSS), Normalised Difference Measure (NDM) and Max\u2013Min Ratio (MMR) using Multinomial Naive Bayes (MNB), Support-Vector Machines (SVMs) and k-Nearest Neighbour (KNN) classifiers on four benchmark data sets. These data sets are Reuters-21578, 20-Newsgroup, Mini 20-Newsgroup and Polarity. The experiments were carried out for six different feature sizes which are 10, 30, 50, 100, 300 and 500. Experimental results show that the performance of EFS method is more successful than the other nine methods in most cases according to micro- F1 and macro- F1 scores.<\/jats:p>","DOI":"10.1177\/0165551521991037","type":"journal-article","created":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T08:27:07Z","timestamp":1618302427000},"page":"59-78","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":80,"title":["A novel filter feature selection method for text classification: Extensive Feature Selector"],"prefix":"10.1177","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8919-6481","authenticated-orcid":false,"given":"Bekir","family":"Parlak","sequence":"first","affiliation":[{"name":"Department of Computer Engineering Faculty of Technology, Amasya University, Turkey"}]},{"given":"Alper Kursat","family":"Uysal","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty 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