Multi-label Feature Selection Techniques for Hierarchical Multi-label Protein Function Prediction

Autor(es)

Cerri, Ricardo ; Mantovani, Rafael G. ; Basgalupp, Marcio P. ; de Carvalho, Andre C. P. L. F. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 7-pg., 2018-01-01.

Resumo

Protein Function Prediction is a complex Hierarchical Multi-label Classification task where the functional classes involved are organized in a hierarchy. While many Machine Learning methods have been proposed for this task, very few studies were performed for feature selection in such hierarchical scenarios. In this paper, we investigate feature selection techniques for hierarchical multi-label classification of protein functions. As decision trees are natural feature selectors, we rely on a hierarchical multi-label decision tree induction algorithm to extract features represented by the internal nodes of the tree. We also investigated the performance of a ReliefF-based non-hierarchical multi-label feature selection technique on the hierarchical scenario. We tested the different techniques on two classifiers, based on neural networks and genetic algorithms. The experimental results show that, in very few cases, the existing feature selection techniques were able to improve the classifiers performances, showing the need for developing feature selectors specifically to consider hierarchical class relationships. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 15/14300-1 - Classificação hierárquica de elementos transponíveis utilizando aprendizado de máquina
• Beneficiário: Ricardo Cerri
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 12/23114-9 - Uso de meta-aprendizado para ajuste de parâmetros em problemas de classificação
• Beneficiário: Rafael Gomes Mantovani
• Modalidade de apoio: Bolsas no Brasil - Doutorado