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Journal of Classification
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02-04-2019

Multiclass Classification Based on Multi-criteria Decision-making

Authors: Hossein Baloochian, Hamid Reza Ghaffary

Published in: Journal of Classification | Issue 1/2019

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Abstract

Lots of real-world problems require multiclass classification. Since most general classification methods are originally introduced for binary problems (including two classes), they should be extended to multiclass problems. A solution proposed for multiclass problems is to decompose such problems to several binary ones and then combine the results obtained from smaller problems as a tree-based structure to obtain the final solution. In this study, a novel method which uses VlseKriterijumska optimizacija I Kompromisno Resenje multi-criteria decision-making was proposed to build the best directed binary tree with minimum error. The proposed method is independent of classifier; nevertheless, in the current experiments, the support vector machine was employed as the base classifier. The proposed method was tested on datasets and the results were compared with other methods. It can be seen that it improves precision of predictions significantly.
previous article Robustification of Gaussian Bayes Classifier by the Minimum β-Divergence Method
next article A New Method for Classifying Random Variables Based on Support Vector Machine

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Metadata
Title
Multiclass Classification Based on Multi-criteria Decision-making
Authors
Hossein Baloochian
Hamid Reza Ghaffary
Publication date
02-04-2019
Publisher
Springer US
Published in
Journal of Classification / Issue 1/2019
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-018-9286-6

Other articles of this Issue 1/2019

A New Method for Classifying Random Variables Based on Support Vector Machine

Editorial: Journal of Classification Vol 36-1

Optimal Landmark Point Selection Using Clustering for Manifold Modeling and Data Classification

Robustification of Gaussian Bayes Classifier by the Minimum β-Divergence Method

Growth Mixture Modeling with Measurement Selection

Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering

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