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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Vydané v:Entropy (Basel, Switzerland) Ročník 22; číslo 4; s. 492
Hlavní autori: Estrela, Gustavo, Gubitoso, Marco Dimas, Ferreira, Carlos Eduardo, Barrera, Junior, Reis, Marcelo S.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 24.04.2020
MDPI
Predmet:
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
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Shrnutí: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 fact that the costs of chains in that lattice describe U-shaped curves. Minimization of such cost function is known as the U-curve problem. Recently, a study proposed U-Curve Search (UCS), an optimal algorithm for that problem, which was successfully used for feature selection. However, despite of the algorithm optimality, the UCS required time in computational assays was exponential on the number of features. Here, we report that such scalability issue arises due to the fact that the U-curve problem is NP-hard. In the sequence, we introduce the Parallel U-Curve Search (PUCS), a new algorithm for the U-curve problem. In PUCS, we present a novel way to partition the search space into smaller Boolean lattices, thus rendering the algorithm highly parallelizable. We also provide computational assays with both synthetic data and Machine Learning datasets, where the PUCS performance was assessed against UCS and other golden standard algorithms in feature selection.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e22040492