Feature selection for multi-label learning

Author(s)

Newton Spolaôr

• Document type: Doctoral Thesis
• Press: São Carlos.
• Institution: Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação
• Defense date: 24 set. 2014

Examining board members

Maria Carolina Monard; Estevam Rafael Hruschka Júnior; Huei Diana Lee; Ana Carolina Lorena; Carlos Alberto Alves Meira

• Advisor: Maria Carolina Monard; Huei Diana Lee

Abstract

Irrelevant and/or redundant features in data can deteriorate the performance of the classifiers built from this data by machine learning algorithms. The aim of feature selection algorithms consists in identifying these features and removing them from data before constructing classifiers. Feature selection in single-label data, in which each instance in the training set is associated with only one label, has been widely studied in the literature. However, this is not the case for multi-label data, in which each instance is associated with a set of labels. Moreover, as multi-label data usually exhibit relationships among the labels in the set of labels, machine learning algorithms should take thiis relatinship into account. Therefore, label dependence should also be explored by multi-label feature selection algorithms. The filter approach is one of the most usual approaches considered by feature selection algorithms, as it has potentially lower computational cost than approaches and uses general properties from data to calculate feature importance measures, such as the feature-class correlation. The hypothesis of this work is that feature selection algorithms which consider label dependence will perform better than the ones that disregard label dependence. To this end, ths work proposes and develops filter approach multi-label feature selection algorithms which take into account relations among labels. In particular, we proposed two methods that take into account these relations by performing label construction and adapting the single-label feature selection algorith RelieF. These methods were experimentally evaluated showing good performance in terms of feature reduction and predictability of the classifiers built using the selected features. (AU)

• FAPESP's process: 11/02393-4 - Feature Selection for Multi-label Learning
• Grantee: Newton Spolaôr
• Support type: Scholarships in Brazil - Doctorate