An instance voting approach to feature selection

Volume: 504, Pages: 449 - 469
Published: Dec 1, 2019
Abstract
In this work, we address the problem of supervised feature selection (FS) for high-dimensional datasets with a small number of instances. Here, we propose a novel heuristic FS approach, Conditional Priority Coverage Maximization (CPCM) which seeks to leverage the local information provided by the small set of instances. We define the vote assigned by an instance to a feature as the local relevance of the latter. Also, we show that the proposed...
Paper Details
Title
An instance voting approach to feature selection
Published Date
Dec 1, 2019
Volume
504
Pages
449 - 469
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