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Undirected cyclic graph based multiclass pair-wise classifier: Classifier number reduction maintaining accuracy
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DOI：10.1016/j.neucom.2015.07.078.png)
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摘要
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Supervised classification approaches try to classify correctly the new unlabelled examples based on a set of well-labelled samples. Nevertheless, some classification methods were formulated for binary classification problems and has difficulties for multi-class problems. Binarization strategies decompose the original multi-class dataset into multiple two-class subsets. For each new sub-problem a classifier is constructed. One-vs-One is a popular decomposition strategy that in each sub-problem discriminates the cases that belong to a pair of classes, ignoring the remaining ones. One of its drawbacks is that it creates a large number of classifiers, and some of them are irrelevant. In order to reduce the number of classifiers, in this paper we propose a new method called Decision Undirected Cyclic Graph. Instead of making the comparisons of all the pair of classes, each class is compared only with other two classes; evolutionary computation is used in the proposed approach in order to obtain suitable class pairing. In order to empirically show the performance of the proposed approach, a set of experiments over four popular Machine Learning algorithms are carried out, where our new method is compared with other well-known decomposition strategies of the literature obtaining promising results. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Machine learning
Supervised classification
Decomposition strategies
One-vs-One
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6.5
论文数:
2.5W
被引数:
6.5W
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