A review on the combination of binary classifiers in multiclass problems

• Type: Article in International Scientific Journal
• Date: 2008

Authors

Carolina Lorena (Author) Other; Andre Carvalho (Author) Other; João Gama (Author) FEP

Journal

Title: Artificial Intelligence Review

Vol. 30 No. 1

Pages: 19-37

ISSN: 0269-2821

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 210 Citations

ISI Web of Science

Scopus - 249 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-003-TB9

DOI: 10.1007/s10462-009-9114-9

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 19