Investigation of Bioinspired Approaches for Hierarchical Multilabel Classification Problems

• Grant number: 09/17401-2
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: June 01, 2010
• End date: December 31, 2013
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: André Carlos Ponce de Leon Ferreira de Carvalho
• Grantee: Ricardo Cerri
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated scholarship(s): 11/22321-8 - A genetic algorithm for hierarchical multi-label classification problems, BE.EP.DR

Abstract

The objective of this project is to investigate and develop strategies based on bioinspired algorithms for hierarchical multilabel classification problems. Unlike non-hierarchical classification, where there are no hierarchical relationships between the classes involved in the problem, in the hierarchical classification the classes can be sub-classes or super-classes of other classes. Among the hierarchical problems, still exist, especially in the field of bioinformatics, a large number of problems where two or more classes can be associated with the same example. These types of problems are named hierarchical multilabel. The development of effective strategies to deal with these problems is a topic of current research. For this project, it is proposed the use of bioinspired approaches to the task of hierarchical multilabel classification of biological data. Bioinspired approaches are relatively unexplored in this type of classification and showed good results in simpler classification problems.

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

CERRI, Ricardo. Neural networks and genetic algorithms for hierarchical multi-label classification. 2013. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.