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

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
Main Authors: Gustavo Estrela, Marco Dimas Gubitoso, Carlos Eduardo Ferreira, Junior Barrera, Marcelo S. Reis
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Entropy
Subjects:
machine learning
supervised learning
information theory
mean conditional entropy
feature selection
classifier design
Online Access:https://www.mdpi.com/1099-4300/22/4/492

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https://www.mdpi.com/1099-4300/22/4/492

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