Fast feature selection aimed at high-dimensional data via hybrid-sequential-ranked searches |
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| Authors: | R Ruiz JC Riquelme JS Aguilar-Ruiz M García-Torres |
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| Affiliation: | 1. School of Engineering, Pablo de Olavide University, Ctra. Utrera km. 1, 41013 Seville, Spain;2. Department of Computer Science, University of Seville, Avda. Reina Mercedes s/n, 41012 Seville, Spain;1. AstraZeneca R&D, Södertälje, Sweden;2. Department of Anesthesia and Intensive Care, Academic Hospital, Uppsala, Sweden;1. College of Marine Life Science, Ocean University of China, Yushan Road, Qingdao 266003, PR China;2. College of Mathematical Science, Ocean University of China, Songling Road, Qingdao 266100, PR China;3. College of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, PR China;1. Centro de Bioplantas, University of Ciego de Ávila, Cuba;2. University of Pablo de Olavide, Sevilla, Spain;1. Department of Animation and Game Design, Shu-Te University, Kaohsiung, Taiwan;2. Department of Information Management, National Yunlin University of Science and Technology, Yunlin, Taiwan |
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| Abstract: | We address the feature subset selection problem for classification tasks. We examine the performance of two hybrid strategies that directly search on a ranked list of features and compare them with two widely used algorithms, the fast correlation based filter (FCBF) and sequential forward selection (SFS). The proposed hybrid approaches provide the possibility of efficiently applying any subset evaluator, with a wrapper model included, to large and high-dimensional domains. The experiments performed show that our two strategies are competitive and can select a small subset of features without degrading the classification error or the advantages of the strategies under study. |
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