An Efficient, Parallelized Algorithm for Optimal Conditional Entropy-Based Feature Selection

In Machine Learning, feature selection is an important step in classifier design. It consists of finding a subset of features that is optimum for a given cost function. One possibility to solve feature selection is to organize all possible feature subsets into a Boolean lattice and to exploit the fa...

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Bibliographic Details
Published in:	Entropy (Basel, Switzerland) Vol. 22; no. 4; p. 492
Main Authors:	Estrela, Gustavo, Gubitoso, Marco Dimas, Ferreira, Carlos Eduardo, Barrera, Junior, Reis, Marcelo S.
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 24.04.2020 MDPI
Subjects:	Algorithms Boolean Boolean algebra classifier design Cost function Experiments feature selection Genetic algorithms Graph representations Hypotheses information theory Lattices Machine learning mean conditional entropy Optimization Parallel processing Random variables Searching supervised learning
ISSN:	1099-4300, 1099-4300
Online Access:	Get full text
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