<abstract xmlns="http://www.w3.org/1999/xhtml"><p> We propose new methods for support vector machines using a tree architecture for multi-class classification. In each node of the tree, we select an appropriate binary classifier, using entropy and generalization error estimation, then group the examples into positive and negative classes based on the selected classifier, and train a new classifier for use in the classification phase. The proposed methods can work in time complexity between O(log<sub>2</sub> N) and O(N), where N is the number of classes. We compare the performance of our methods with traditional techniques on the UCI machine learning repository using 10-fold cross-validation. The experimental results show that the methods are very useful for problems that need fast classification time or those with a large number of classes, since the proposed methods run much faster than the traditional techniques but still provide comparable accuracy.</p></abstract>

We propose new methods for support vector machines using a tree architecture for multi-class classification. In each node of the tree, we select an appropriate binary classifier, using entropy and generalization error estimation, then group the examples into positive and negative classes based on the selected classifier, and train a new classifier for use in the classification phase. The proposed methods can work in time complexity between O(log2 N) and O(N), where N is the number of classes. We compare the performance of our methods with traditional techniques on the UCI machine learning repository using 10-fold cross-validation. The experimental results show that the methods are very useful for problems that need fast classification time or those with a large number of classes, since the proposed methods run much faster than the traditional techniques but still provide comparable accuracy.

Efficient Decision Trees for Multi–Class Support Vector Machines Using Entropy and Generalization Error Estimation

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

We propose new methods for support vector machines using a tree architecture for multi-class classification. In each node of the tree, we select an appropriate...

Efficient Decision Trees for Multi–Class Support Vector Machines Using Entropy and Generalization Error Estimation

Online veröffentlicht: 11. Jan. 2019

Seitenbereich: 705 - 717

Eingereicht: 03. Okt. 2017

Akzeptiert: 18. Mai 2018

DOI: https://doi.org/10.2478/amcs-2018-0054

Schlüsselwörter
support vector machine, multi-class classification, generalization error, entropy, decision tree

© 2019 Pittipol Kantavat, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Efficient Decision Trees for Multi–Class Support Vector Machines Using Entropy and Generalization Error Estimation

Online veröffentlicht: 11. Jan. 2019

Seitenbereich: 705 - 717

Eingereicht: 03. Okt. 2017

Akzeptiert: 18. Mai 2018

DOI: https://doi.org/10.2478/amcs-2018-0054

Schlüsselwörtersupport vector machine, multi-class classification, generalization error, entropy, decision tree

© 2019 Pittipol Kantavat, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
support vector machine, multi-class classification, generalization error, entropy, decision tree