Emerging topics and challenges of learning from noisy data in nonstandard classification: a survey beyond binary class noise

Autor(es)

Prati, Ronaldo C. ^[1] ; Luengo, Julian ^[2] ; Herrera, Francisco ^[2]

Afiliação do(s) autor(es)

^[1] Fed Univ ABC UFABC, Ctr Math Comp Sci & Cognit CMCC, Santo Andre, SP - Brazil

^[2] Univ Granada UGR, Dept Comp Sci & AI DECSAI, Granada - Spain

• Tipo de documento: Artigo Científico
• Fonte: KNOWLEDGE AND INFORMATION SYSTEMS; v. 60, n. 1, p. 63-97, JUL 2019.
• Citações Web of Science: 0

Resumo

The problem of class noisy instances is omnipresent in different classification problems. However, most of research focuses on noise handling in binary classification problems and adaptations to multiclass learning. This paper aims to contextualize noise labels in the context of non-binary classification problems, including multiclass, multilabel, multitask, multi-instance ordinal and data stream classification. Practical considerations for analyzing noise under these classification problems, as well as trends, open-ended problems and future research directions are analyzed. We believe this paper could help expand research on class noise handling and help practitioners to better identify the particular aspects of noise in challenging classification scenarios. (AU)

• Processo FAPESP: 15/20606-6 - Rótulos imprecisos em Aprendizado de Máquina: Medidas de avaliação e algoritmos de aprendizado de máquina
• Beneficiário: Ronaldo Cristiano Prati
• Modalidade de apoio: Bolsas no Exterior - Pesquisa