Idioma:

Classification in the Presence of Label Noise: A Survey

Frenay, Benoit ; Verleysen, Michel

IEEE transaction on neural networks and learning systems, 2014-05, Vol.25 (5), p.845-869

New York, NY: IEEE

Texto completo disponível

Citações Citado por

Enviar para

Título:
Classification in the Presence of Label Noise: A Survey
Autor: Frenay, Benoit ; Verleysen, Michel
Assuntos: Algorithms ; Applied sciences ; Categories ; Class noise ; Classification ; Computer science; control theory; systems ; Context ; Data processing. List processing. Character string processing ; Design of experiments ; Exact sciences and technology ; label noise ; Labeling ; Labels ; Learning ; Memory organisation. Data processing ; mislabeling ; Neural networks ; Noise ; Noise measurement ; Reliability ; robust methods ; Software ; survey ; Taxonomy ; Training
É parte de: IEEE transaction on neural networks and learning systems, 2014-05, Vol.25 (5), p.845-869
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ObjectType-Article-2
ObjectType-Feature-1
Descrição: Label noise is an important issue in classification, with many potential negative consequences. For example, the accuracy of predictions may decrease, whereas the complexity of inferred models and the number of necessary training samples may increase. Many works in the literature have been devoted to the study of label noise and the development of techniques to deal with label noise. However, the field lacks a comprehensive survey on the different types of label noise, their consequences and the algorithms that consider label noise. This paper proposes to fill this gap. First, the definitions and sources of label noise are considered and a taxonomy of the types of label noise is proposed. Second, the potential consequences of label noise are discussed. Third, label noise-robust, label noise cleansing, and label noise-tolerant algorithms are reviewed. For each category of approaches, a short discussion is proposed to help the practitioner to choose the most suitable technique in its own particular field of application. Eventually, the design of experiments is also discussed, what may interest the researchers who would like to test their own algorithms. In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g., confidences on labels.
Editor: New York, NY: IEEE
Idioma: Inglês

Links

Voltar para lista de resultados

Realização: Logos de Redes Sociais:

Classification in the Presence of Label Noise: A Survey

Frenay, Benoit ; Verleysen, Michel

New York, NY: IEEE

Buscando em bases de dados remotas. Favor aguardar.