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

Author(s)

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

• Document type: Journal article
• Source: 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 7-pg., 2018-01-01.

Abstract

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)

• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 15/14300-1 - Hierarchical classification of transposable elements using machine learning
• Grantee: Ricardo Cerri
• Support Opportunities: Regular Research Grants
• FAPESP's process: 12/23114-9 - Use of meta-learning for parameter tuning for classification problems
• Grantee: Rafael Gomes Mantovani
• Support Opportunities: Scholarships in Brazil - Doctorate