Artificial Intelligence Review

, 30:19

A review on the combination of binary classifiers in multiclass problems

Authors

    • Centro de Matemática, Computação e CogniçãoUniversidade Federal do ABC
  • André C. P. L. F. de Carvalho
    • Departamento de Ciências de Computação, Instituto de Ciências Matemáticas e de ComputaçãoUniversidade de São Paulo
  • João M. P. Gama
    • Laboratório de Inteligência Artificial e Ciência de ComputadoresUniversidade do Porto
Article

DOI: 10.1007/s10462-009-9114-9

Cite this article as:
Lorena, A.C., de Carvalho, A.C.P.L.F. & Gama, J.M.P. Artif Intell Rev (2008) 30: 19. doi:10.1007/s10462-009-9114-9

Abstract

Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of problems with only two classes, also named binary classification problems. However, many problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.

Keywords

Machine learningSupervised learningMulticlass classification
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Copyright information

© Springer Science+Business Media B.V. 2009