An adaptive heuristic for feature selection based on complementarity

Singha, S and Shenoy, P P (2018) An adaptive heuristic for feature selection based on complementarity. Machine Learning, 107 (12). pp. 2027-2071.

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Abstract

Feature selection is a dimensionality reduction technique that helps to improve data visualization, simplify learning, and enhance the efficiency of learning algorithms. The existing redundancy-based approach, which relies on relevance and redundancy criteria, does not account for feature complementarity. Complementarity implies information synergy, in which additional class information becomes available due to feature interaction. We propose a novel filter-based approach to feature selection that explicitly characterizes and uses feature complementarity in the search process. Using theories from multi-objective optimization, the proposed heuristic penalizes redundancy and rewards complementarity, thus improving over the redundancy-based approach that penalizes all feature dependencies. Our proposed heuristic uses an adaptive cost function that uses redundancy--complementarity ratio to automatically update the trade-off rule between relevance, redundancy, and complementarity. We show that this adaptive approach outperforms many existing feature selection methods using benchmark datasets.

Affiliation: Indian School of Business
ISB Creators:
ISB CreatorsORCiD
Singha, Shttps://orcid.org/0000-0003-3794-127X
Item Type: Article
Uncontrolled Keywords: Dimensionality reduction, Feature selection, Classification Feature complementarity, Adaptive heuristic
Subjects: Business Analytics
Applied Statistics and Computing
Depositing User: Veeramani R
Date Deposited: 26 Jul 2018 12:03
Last Modified: 02 May 2019 05:33
URI: http://eprints.exchange.isb.edu/id/eprint/576
Publisher URL: https://doi.org/10.1007/s10994-018-5728-y
Publisher OA policy: http://www.sherpa.ac.uk/romeo/issn/0885-6125/
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