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Informative variable identifier: Expanding interpretability in feature selection
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DOI：10.1016/j.patcog.2019.107077.png)
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Abstract
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There is nowadays an increasing interest in discovering relationships among input variables (also called features) from data to provide better interpretability, which yield more confidence in the solution and provide novel insights about the nature of the problem at hand. We propose a novel feature selection method, called Informative Variable Identifier (IVI), capable of identifying the informative variables and their relationships. It transforms the input-variable space distribution into a coefficient-feature space using existing linear classifiers or a more efficient weight generator that we also propose, Covariance Multiplication Estimator (CME). Informative features and their relationships are determined analyzing the joint distribution of these coefficients with resampling techniques. IVI and CME select the informative variables and then pass them on to any linear or nonlinear classifier. Experiments show that the proposed approach can outperform state-of-art algorithms in terms of feature identification capabilities, and even in classification performance when subsequent classifiers are used. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Feature selection
Interpretability
Explainable machine learning
Resampling
Classification
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