Efficient Decision Trees for Multi-class Support Vector Machines Using Large Centroid Distance Grouping

Authors

  • Pittipol Kantavat Chulalongkorn University
  • Patoomsiri Songsiri Chulalongkorn University
  • Boonserm Kijsirikul Chulalongkorn University

DOI:

https://doi.org/10.4186/ej.2022.26.5.13

Keywords:

Support Vector Machine (SVM), multiclass classification, data centroid, large centroid distance grouping, decision tree

Abstract

We propose a new technique for support vector machines (SVMs) in tree structures for multiclass classification. For each tree node, we select an appropriate binary classifier using data class centroids and their in-between distances, categorize the training examples into positive and negative groups of classes and train a new classifier. The proposed technique is fast-trained and can classify an output class data with a complexity between O(log2N) and O(N) where N is the number of classes. The 10-fold cross-validation experimental results show that the performance of our methods is comparable to that of traditional techniques and required less decision times. Our proposed technique is suitable for problems with a large number of classes due to its advantages of requiring less training time and computational complexity.

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Author Biographies

Pittipol Kantavat

Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand

Patoomsiri Songsiri

Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand

Boonserm Kijsirikul

Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand

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Published In
Vol 26 No 5, May 31, 2022
How to Cite
[1]
P. Kantavat, P. Songsiri, and B. Kijsirikul, “Efficient Decision Trees for Multi-class Support Vector Machines Using Large Centroid Distance Grouping”, Eng. J., vol. 26, no. 5, pp. 13-23, May 2022.