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. ISSN 2730-9916

Full text not available from this repository. (Request a copy)


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.

Item Type: Article
Subjects: Information Systems
Date Deposited: 16 Jun 2019 17:06
Last Modified: 06 Jul 2023 21:52

Actions (login required)

View Item
View Item