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An improved multiclass support vector machine classifier using reduced hyper-plane with skewed binary tree

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Abstract

Support Vector Machine (SVM) is mainly used to classify the data into two categories. To solve the multi-category problems using SVM, researchers used two approaches. The first approach based on solving multiple SVM binary classifiers, whereas another approach based on solving a single optimization problem. In this paper, we have used the first approach and proposed an Efficient Multiclass Support Vector Machine (ESVM) algorithm using a skewed binary tree. To construct the skewed binary tree, no extra efforts are required as compared to the binary tree approach. The algorithm is tested on the benchmark data sets, and the results are compared with both the multiclass approaches of SVM. The ESVM’s results are compared with five techniques of solving multiple binary SVM classifiers and four techniques of solving a single optimization problem. The comparative experiments prove the efficiency of the ESVM in terms of its accuracy as compared to other contemporary algorithms. Further, ESVM is successfully applied for classification of the email dataset into positive, negative and neutral sentiments.

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Acknowledgments

I am very thankful to Dr. Piyush G. Gampawar for his valuable suggestions in the paper writing. I am very grateful to the reviewers of the paper as there valuable suggestions were very helpful for drafting the well organized paper.

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Authors and Affiliations

  1. Department of Information Technology, Priyadarshini College of Engineering, Nagpur, Maharashtra, India

    Pranjal S. Bogawar

  2. Department of Information Technology, Yashvantrao Chavan College of Engineering, Nagpur, Maharashtra, India

    Kishor K. Bhoyar

Authors
  1. Pranjal S. Bogawar
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  2. Kishor K. Bhoyar
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Correspondence to Pranjal S. Bogawar.

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Bogawar, P.S., Bhoyar, K.K. An improved multiclass support vector machine classifier using reduced hyper-plane with skewed binary tree. Appl Intell 48, 4382–4391 (2018). https://doi.org/10.1007/s10489-018-1218-y

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  • DOI: https://doi.org/10.1007/s10489-018-1218-y

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