Estratégias para a Combinação de Classificadores Binários em Soluções Multiclasses

Abstract

Several problems involve the classification of data into categories, also called classes. Given a dataset 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 problems. However, several problems require
the discrimination of examples into more than two categories or classes. This paper surveys strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final classification.