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• W2053608218 abstract "In the feature selection community, filters are quite popular. Design of a filter depends on two parameters, namely the objective function and the metric it employs for estimating the feature-to-class (relevance) and feature-to-feature (redundancy) association. Filter designers pay relatively more attention towards the objective function. But a poor metric can overshadow the goodness of an objective function. The metrics that have been proposed in the literature estimate the relevance and redundancy differently, thus raising the question: can the metric estimating the association between two variables improve the feature selection capability of a given objective function or in other words a filter. This paper investigates this question. Mutual information is the metric proposed for measuring the relevance and redundancy between the features for the mRMR filter [1] while the MBF filter [2] employs correlation coefficient. Symmetrical uncertainty, a variant of mutual information, is used by the fast correlation-based filter (FCBF) [3]. We carry out experiments on mRMR, MBF and FCBF filters with three different metrics (mutual information, correlation coefficient and diff-criterion) using three binary data sets and four widely used classifiers. We find that MBF׳s performance is much better if it uses diff-criterion rather than correlation coefficient while mRMR with diff-criterion demonstrates performance better or comparable to mRMR with mutual information. For the FCBF filter, the diff-criterion also exhibits results much better than mutual information." @default.
• W2053608218 created "2016-06-24" @default.
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• W2053608218 date "2014-11-01" @default.
• W2053608218 modified "2023-09-27" @default.
• W2053608218 title "Impact of a metric of association between two variables on performance of filters for binary data" @default.
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• W2053608218 doi "https://doi.org/10.1016/j.neucom.2014.05.066" @default.
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