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Multi-objective evolutionary methods for hierarchical and multi-label classification

Grant number: 16/50457-5
Support type:Regular Research Grants
Duration: June 01, 2017 - May 31, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Cooperation agreement: University of Surrey
Mobility Program: SPRINT - Projetos de pesquisa - Mobilidade
Principal Investigator:Ricardo Cerri
Grantee:Ricardo Cerri
Principal investigator abroad: Yaochu Jin
Institution abroad: University of Surrey, England
Home Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
Associated research grant:15/14300-1 - Hierarchical classification of transposable elements using machine learning, AP.R

Abstract

In conventional classification, an instance is classified in just one among two or more classes. These problems are called single-label classification problems. However, there are more complex problems in which an instance can be classified in two or more classes simultaneously. These are known in the Machine Learning literature as multi-label classification problems. When the classes involved are organized in a hierarchy, the task is even more challenging, and is known as hierarchical classification. Many practical applications are related to hierarchical and multi-label classification, like the classification of images, documents and music, and protein function prediction. These are very challenging tasks due the difficulty in considering the relationships between the many classes of the problem during training. Together with this, the unbalance of the datasets harm the performances of the classifiers proposed in the literature. Given the high space of possible classes, we propose the development of Multi-objective Evolutionary Methods for the generation of multi-label and hierarchical classification rules. The rules generated should satisfy the requisites of interpretability and good performance. The proposed methods will be compared with state-of-the-art methods in the literature, using hierarchical and multi-label datasets from the bioinformatics domain. The method will be evaluated with measures specifically proposed for hierarchical and multi-label problems. (AU)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
CERRI, RICARDO; BASGALUPP, MARCIO P.; BARROS, RODRIGO C.; DE CARVALHO, ANDRE C. P. L. F. Inducing Hierarchical Multi-label Classification rules with Genetic Algorithms. APPLIED SOFT COMPUTING, v. 77, p. 584-604, APR 2019. Web of Science Citations: 1.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.