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

Autor(es)

Estrela, Gustavo ^{[1, 2]} ; Gubitoso, Marco Dimas ^[2] ; Ferreira, Carlos Eduardo ^[2] ; Barrera, Junior ^[2] ; Reis, Marcelo S. ^[1]

Afiliação do(s) autor(es)

^[1] Inst Butantan, Ctr Toxins Immune Response & Cell Signaling CeT, Lab Ciclo Celular, BR-05503900 Butanta, SP - Brazil

^[2] Univ Sao Paulo, Inst Matemat & Estat, BR-05503900 Sao Paulo, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Entropy; v. 22, n. 4 APR 2020.
• Citações Web of Science: 0

Resumo

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. (AU)

• Processo FAPESP: 16/25959-7 - Projeto de algoritmos baseados em florestas de posets para o problema de otimização U-curve
• Beneficiário: Gustavo Estrela de Matos
• Linha de fomento: Bolsas no Brasil - Iniciação Científica
• Processo FAPESP: 13/07467-1 - CeTICS - Centro de Toxinas, Imuno-Resposta e Sinalização Celular
• Beneficiário: Hugo Aguirre Armelin
• Linha de fomento: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 15/01587-0 - Armazenagem, modelagem e análise de sistemas dinâmicos para aplicações em e-Science
• Beneficiário: João Eduardo Ferreira
• Linha de fomento: Auxílio à Pesquisa - Programa eScience e Data Science - Temático