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Progress in Artificial Intelligence

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23.11.2018 | Review

A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations

verfasst von: David Charte, Francisco Charte, Salvador García, Francisco Herrera

Erschienen in: Progress in Artificial Intelligence | Ausgabe 1/2019

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Abstract

Machine learning is a field which studies how machines can alter and adapt their behavior, improving their actions according to the information they are given. This field is subdivided into multiple areas, among which the best known are supervised learning (e.g., classification and regression) and unsupervised learning (e.g., clustering and association rules). Within supervised learning, most studies and research are focused on well-known standard tasks, such as binary classification, multi-class classification and regression with one dependent variable. However, there are many other less known problems. These are what we generically call nonstandard supervised learning problems. The literature about them is much more sparse, and each study is directed to a specific task. Therefore, the definitions, relations and applications of this kind of learners are hard to find. The goal of this paper is to provide the reader with a broad view on the distinct variations of nonstandard supervised problems. A comprehensive taxonomy summarizing their traits is proposed. A review of the common approaches followed to accomplish them, and their main applications are provided as well.

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Titel: A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations
verfasst von: David Charte
Francisco Charte
Salvador García
Francisco Herrera
Publikationsdatum: 23.11.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Progress in Artificial Intelligence / Ausgabe 1/2019
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI: https://doi.org/10.1007/s13748-018-00167-7

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