Feature Selection for multi-labeled variables via Dependency Maximization

Published: Feb 10, 2019
Abstract
Feature selection and reducing the dimensionality of data is an essential step in data analysis. In this work, we propose a new criterion for feature selection that is formulated as conditional information between features given the labeled variable. Instead of using the standard mutual information measure based on Kullback-Leibler divergence, we use our proposed criterion to filter out redundant features for the purpose of multiclass...
Paper Details
Title
Feature Selection for multi-labeled variables via Dependency Maximization
Published Date
Feb 10, 2019
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