Network-based Instance Hardness Measures for Classification Problems

Autor(es)

Torquette, Gustavo ; Basgalupp, Marcio P. ; Ludermir, Teresa B. ; Lorena, Ana Carolina

• Tipo de documento: Artigo Científico
• Fonte: 40TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING; v. N/A, p. 8-pg., 2025-01-01.

Resumo

Instance hardness measures allow one to characterize and understand why some instances are harder to classify than others in a classification dataset. An instance can be hard to classify for different reasons, such as being in an overlapping region of the classes or a region of poor data representativeness. While there are many instance hardness measures in the related literature, they are mainly concerned with measuring class overlap. This paper also addresses measuring sparsity in a dataset by building a proximity graph from data and extracting some network-based measures from the nodes. Experimentally, we show that some of these measures are effective in characterizing instance hardness and complement the ones from the literature by measuring the density of the regions where the instances are located. (AU)

• Processo FAPESP: 22/07458-1 - Construção e seleção automática de algoritmos de aprendizado de máquina
• Beneficiário: Márcio Porto Basgalupp
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 21/06870-3 - Além da seleção de algoritmos: meta-aprendizado para análise e entendimento de dados e algoritmos
• Beneficiário: Ana Carolina Lorena
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores - Fase 2
• Processo FAPESP: 20/09835-1 - IARA - Inteligência Artificial Recriando Ambientes
• Beneficiário: André Carlos Ponce de Leon Ferreira de Carvalho
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa em Engenharia