Learning classifier system for multi-label classification

Author(s)

Rosane Maria Maffei Vallim

• Document type: Master's Dissertation
• Press: São Carlos.
• Institution: Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB)
• Defense date: 2009-07-27

Examining board members

André Carlos Ponce de Leon Ferreira de Carvalho; Gustavo Enrique de Almeida Prado Alves Batista; Renato Tinós

• Advisor: André Carlos Ponce de Leon Ferreira de Carvalho
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Indexed in: Banco de Dados Bibliográficos da USP-DEDALUS; Biblioteca Digital de Teses e Dissertações - USP
• Location: Universidade de São Paulo. Instituto de Ciências Matemáticas e de Computação. Biblioteca Prof. Achille Bassi; T; V188sc

Abstract

Classification is probably the most studied task in the Machine Learning area, with applications in a broad number of real problems like text categorization, medical diagnosis, bioinformatics and even comercial and industrial applications. Generally, classification problems can be categorized considering the number of class labels associated to each input instance. The most studied approach by the community of Machine Learning is the one that considers mutually exclusive classes. However, there is a large variety of important problems in which each instance can be associated to more than one class label. This problems are called multi-label classification problems. Learning Classifier Systems (LCS) are a technique for rule induction which uses a Genetic Algorithm as the primary search mechanism. This technique searchs for sets of rules that have high classification accuracy and that are also understandable and interesting on the classification point of view. Although there are several works on LCS for classification problems with mutually exclusive classes, there is no record of an LCS that can deal with the multi-label classification problem. The objective of this work is to propose an LCS for multi-label classification that builds a set of classification rules which achieves results that are efficient and comparable to other multi-label methods. In accordance with this objective this work also presents a review of the themes involved: Learning Classifier Systems and Multi-label Classification (AU)