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Such dimensions can increase the time and computational cost in the learning process and even degenerate the performance of learning tasks. One of the ways to reduce dimensionality is by Feature Selection (FS). The aim of this paper is study the feature selection based on expert knowledge and traditional methods (filter, wrapper and embedded) and analyze their performance in classification tasks. Three datasets related to cancer domain in humans were used for feature selection: Breast Cancer (BC), Primary Tumor (PT) and Central Nervous System (CNS). C4.5, K-Nearest Neighbors, Support Vector Machine and Multi Layer Perceptron were trained with the best subset of features for each cancer dataset. The subset of features selected by the wrapper method presents the best average accuracy in the datasets BC and PT, while the subset of features selected by the embedded method reaches the highest average accuracy in the CNS dataset.<\/jats:p>","DOI":"10.3233\/jifs-169470","type":"journal-article","created":{"date-parts":[[2018,5,1]],"date-time":"2018-05-01T10:18:12Z","timestamp":1525169892000},"page":"2825-2835","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":18,"title":["Feature selection for classification tasks: Expert knowledge or traditional methods?"],"prefix":"10.1177","volume":"34","author":[{"given":"David Camilo","family":"Corrales","sequence":"first","affiliation":[{"name":"Universidad del Cauca, Grupo de Ingenier\u00eda Telem\u00e1tica, Campus Tulc\u00e1n, Popay\u00e1n, Colombia"},{"name":"Universidad Carlos III de Madrid, Departamento de Ciencias de la Computaci\u00f3n e Ingenier\u00eda, Avenida de la Universidad, Legan\u00e9s, Spain"}]},{"given":"Emmanuel","family":"Lasso","sequence":"additional","affiliation":[{"name":"Universidad del Cauca, Grupo de Ingenier\u00eda Telem\u00e1tica, Campus Tulc\u00e1n, Popay\u00e1n, Colombia"}]},{"given":"Agapito","family":"Ledezma","sequence":"additional","affiliation":[{"name":"Universidad Carlos III de Madrid, Departamento de Ciencias de la Computaci\u00f3n e Ingenier\u00eda, Avenida de la Universidad, Legan\u00e9s, Spain"}]},{"given":"Juan Carlos","family":"Corrales","sequence":"additional","affiliation":[{"name":"Universidad del Cauca, Grupo de Ingenier\u00eda Telem\u00e1tica, Campus Tulc\u00e1n, Popay\u00e1n, Colombia"}]}],"member":"179","published-online":{"date-parts":[[2018,5]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2014.2332453"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.17706\/jcp.10.6.396-405"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2013.11.024"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.12.117"},{"key":"e_1_3_2_6_2","article-title":"Chapman & Hall\/CRC","author":"Liu H.","year":"2007","unstructured":"LiuH. and MotodaH., Chapman & Hall\/CRC, Computational Methods of Feature Selection (Chapman & Hall\/Crc Data Mining and Knowledge Discovery Series).2007.","journal-title":"Computational Methods of Feature Selection (Chapman & Hall\/Crc Data Mining and Knowledge Discovery Series)"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9211"},{"key":"e_1_3_2_8_2","article-title":"Irrelevant features and the subset selection problem. 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