Data Complexity Measures for Imbalanced Classification Tasks

Autor(es)

Barella, Victor H. ; Garcia, Luis P. F. ; de Souto, Marcilio P. ; Lorena, Ana C. ; de Carvalho, Andre ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2018-01-01.

Resumo

In imbalanced classification tasks, the training datasets may show class overlapping and classes of low density. In these scenarios, the predictions for the minority class are impaired. Although assessing the imbalance level of a training set is straightforward, it is hard to measure other aspects that may affect the predictive performance of classification algorithms in imbalanced tasks. This paper presents a set of measures designed to understand the difficulty of imbalanced classification tasks by regarding on each class individually. They are adapted from popular data complexity measures for classification problems, which are shown to perform poorly in imbalanced scenarios. Experiments on synthetic datasets with different levels of imbalance, class overlapping and density of the classes show that the proposed adaptations can better explain the difficulty of imbalanced classification tasks. (AU)

• Processo FAPESP: 16/18615-0 - Aprendizado de máquina avançado
• Beneficiário: André Carlos Ponce de Leon Ferreira de Carvalho
• Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 15/01382-0 - Influência do tratamento de dados em algoritmos de classificação
• Beneficiário: Victor Hugo Barella
• Modalidade de apoio: Bolsas no Brasil - Doutorado