A cluster based hybrid feature selection approach

Jaskowiak, Pablo A., and Campello, Ricardo J.G.B. (2015) A cluster based hybrid feature selection approach. In: Proceedings of the 2015 Brazilian Conference on Intelligent Systems. pp. 43-48. From: BRACIS 2015: Brazilian Conference on Intelligent Systems, 4-7 November 2015, Natal, Brazil.

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Abstract

Data collection and storage capacities have increased significantly in the past decades. In order to cope with the increasingly complexity of data, feature selection methods have become an omnipresent preprocessing step in data analysis. In this paper we present a hybrid (filter - wrapper) feature selection method tailored for data classification problems. Our hybrid approach is composed of two stages. In the first stage, a filter clusters features to identify and remove redundancy. In the second stage, a wrapper evaluates different feature subsets produced by the filter, determining the one that produces the best classification performance in terms of accuracy. The effectiveness of our method is demonstrated through an empirical evaluation performed on real-world datasets coming from various sources.

Item ID: 47063
Item Type: Conference Item (Research - E1)
ISBN: 978-1-5090-0016-6
Keywords: feature selection, filter-wrapper, hybrid feature selection, classification, feature clustering
Funders: FAPESP, CNPq
Projects and Grants: FAPESP grant #2011-04247-5, FAPESP grant #2013/18698-4, CNPq grant #301437/2013-8
Date Deposited: 04 Jan 2017 08:04
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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