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As many classifiers cannot deal with the features with large dimensions, the noisy, irrelevant and redundant information must be filtered from the original feature space. On this basis, a random walk algorithm based feature selection method (called RWFS) is proposed in this paper. Firstly, an optimal feature selection method (called OPFS) is used to select some features from the training set. Secondly, the redundant features are filtered by combining the random walk algorithm and a pre-determined threshold. Moreover, in order to search the optimal threshold, an improved artificial bee colony method (called IMABC) is proposed for parameter optimization. In the experiments, support vector machine (SVM) and k-Nearest Neighbor (KNN) classifiers are used on four corpuses. The experimental results show that, the proposed method is significantly superior to six typical feature selections, and can greatly reduce the dimension of vector space while guaranteeing the classification accuracy as measured by F1 measurement.<\/jats:p>","DOI":"10.3233\/jifs-151191","type":"journal-article","created":{"date-parts":[[2017,1,17]],"date-time":"2017-01-17T13:01:12Z","timestamp":1484658072000},"page":"115-126","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Novel feature selection method based on\u00a0random walk and artificial bee colony"],"prefix":"10.1177","volume":"32","author":[{"given":"Lizhou","family":"Feng","sequence":"first","affiliation":[{"name":"School of Polytechnic, Tianjin University of Finance and Economics, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youwei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information, Central University of Finance and Economics, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanli","family":"Zuo","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2017,1,13]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-004-0177-2"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.20965\/jaciii.2011.p0125"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-009-0256-5"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2009.06.009"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2011.04.014"},{"key":"e_1_3_2_7_2","first-page":"1","article-title":"Term frequency combined hybrid feature selection method for spam filtering","author":"Liu Y.","year":"2014","unstructured":"LiuY., WangY., FengL., et al., Term frequency combined hybrid feature selection method for spam filtering, Pattern Analysis and Applications (2014), 1\u201315.","journal-title":"Pattern Analysis and Applications"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1162\/089120101300346787"},{"key":"e_1_3_2_9_2","unstructured":"ChangA. 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