Ant-Miner Specializations to tackle Imbalanced Data Sets

Título: Ant-Miner Specializations to tackle Imbalanced Data Sets

Autores: Souza, Murilo Zangari de; Pozo, Aurora Trinidad Ramirez; Romão, Wesley

Resumo: A data set is named imbalanced when the classes have not approximately equally representations. Classification algorithms are sensitive of this imbalance and tend to valorize the majority classes and ignore the minority classes, which is a problem when the minority classes are the classes of interest. In this paper we propose two specializations to an efficient and robust classification algorithm inspired by ACO metaheuristic called Ant-Miner. These specializations modify how rules are constructed and evaluated. We compare the results with standard Ant-Miner and C4.5 algorithm. The results show that the proposed algorithms are competitive, finding rules for the minority classes and improve the simplicity of the discovered rule list.

Palavras-chave: Class imbalance; classification algorithms; ACO; Ant-Miner

Páginas: 6

Código DOI: 10.21528/CBIC2013-029

Artigo em pdf: bricsccicbic2013_submission_29.pdf

Arquivo BibTex: bricsccicbic2013_submission_29.bib