Neural networks and genetic algorithms for hierarchical multi-label classification

Author(s)

Ricardo Cerri

• Document type: Doctoral Thesis
• Press: São Carlos.
• Institution: Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB)
• Defense date: 2013-12-05

Examining board members

André Carlos Ponce de Leon Ferreira de Carvalho; Luiz Henrique de Campos Merschmann; Roseli Aparecida Francelin Romero; Bianca Zadrozny; Fernando José von Zuben

• Advisor: André Carlos Ponce de Leon Ferreira de Carvalho

Abstract

conventional classification problems, each example of a dataset is associated with just one among two or more classes. However, there are more complex classification problems where the classes are hierarchically structured, having subclasses and superclasses. In these problems, examples can be simultaneously assigned to classes belonging to two or more paths of a hierarchy, i.e., examples can be classified in many classes located in the same hierarchical level. Such a hierarchy can be structured as a tree or a directed acyclic graph. These problems are known as hierarchical multi-label classification problems, being more difficult due to the high complexity, diversity of solutions, modeling difficulty and data imbalance. Two main approaches are used to deal with these problems, called global and local. In the global approach, only one classifier is induced to deal with all classes simultaneously, and the classification of new examples is done in just one step. In the local approach, a set of classifiers is induced, where each classifier is responsible for the predictions of one class or a set of classes, and the classification of new examples is done in many steps, considering the predictions of all classifiers. In this Thesis, two methods for hierarchical multi-label classification are proposed and investigated. The first one is based on the local approach, and associates a Multi-Layer Perceptron (MLP) to each hierarchical level, being each MLP responsible for the predictions in its associated level. The method is called Hierarchical Multi-Label Classification with Local Multi-Layer Perceptrons (HMC-LMLP). The second method is based on the global approach, and induces hierarchical multi-label classification rules using a Genetic Algorithm. The method is called Hierarchical Multi-Label Classification with a Genetic Algorithm (HMC-GA). Experiments using hierarchies structured as trees showed that HMC-LMLP obtained classification performances superior to the state-of-the-art method in the literature, and superior or competitive performances when using graph-structured hierarchies. The HMC-GA method obtained competitive results with other methods of the literature in both tree and graph-structured hierarchies, being able of inducing, in many cases, smaller and in less quantity rules (AU)

• FAPESP's process: 09/17401-2 - Investigation of Bioinspired Approaches for Hierarchical Multilabel Classification Problems
• Grantee: Ricardo Cerri
• Support Opportunities: Scholarships in Brazil - Doctorate