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 take label noise into account. This paper proposes to fill this gap. Firstly, the definitions and sources of label noise are considered and a taxonomy of the types of label noise is proposed. Secondly, the potential consequences of label noise are discussed. Thirdly, label noise-robust, label noise cleansing and label noise-tolerant algorithms are reviewed. For each category of approaches, a short discussion is proposed in order 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 survey, label noise consists of mislabelled instances: no additional information is assumed to be available, like e.g. confidences on labels.
|Pages (de - à)||845-869|
|Nombre de pages||25|
|journal||IEEE Transactions on Neural Networks and Learning Systems|
|Numéro de publication||5|
|Etat de la publication||Publié - 2014|