Hierarchical multilabel classification techniques

Author(s)

Ricardo Cerri

• 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: 2010-02-23

Examining board members

André Carlos Ponce de Leon Ferreira de Carvalho; Heloisa de Arruda Camargo; Gisele Lobo Pappa

• Advisor: André Carlos Ponce de Leon Ferreira de Carvalho

Abstract

Many of the classification problems described in the literature of Machine Learning and Data Mining are related to data classification where each example to be classified belongs to a finite, and usually small, set of classes located at the same level. There are many classification problems, however, that are of hierarchical nature, where classes can be subclasses or superclasses of other classes. In many hierarchical problems, mainly in the Bioinformatics field, one or more examples can be associated to more than one class simultaneously. These problems are known as hierarchical multilabel classification problems. In this research, different techniques to deal with these kinds of problems were investigated, based on two approaches, named local or Top-Down and global or One-Shot. Three techniques described in the literature were used. The first one, named HMC-BR, is based on the Top-Down approach, and uses a binary classification strategy named One-Against-All. The other two techniques, based on the One-Shot approach, are named C4.5H (an extension of the decision tree induction algorithm C4.5), and Clus-HMC (based on the notion of Predictive Clustering Trees, where decision trees are structured as a hierarchy of clusters). In addition to the techniques described in the literature, two new techniques were proposed, named HMC-LP and HMC-CT. These techniques are hierarchical variations of non-hierarchical multilabel classification techniques. The HMCLP technique uses a label combination strategy and the HMC-CT technique uses a label decomposition strategy. The evaluation of the techniques was performed using specific metrics for this kind of classification. The experimental results showed that the proposed techniques achieved better or similar performances than the techniques described in the literature, depending on the evaluation metric used and on the characteristics of the datasets (AU)

• FAPESP's process: 08/01999-3 - Multi-Label Hierarchical Classification
• Grantee: Ricardo Cerri
• Support Opportunities: Scholarships in Brazil - Master