Enhancing the Ensemble of Exemplar-SVMs for Binary Classification Using Concurrent Selection and Ensemble Learning

Yaobin Qin, Bingzhe Li, David J. Lilja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Support Vector Machines (SVMs) have become one of the most common and popular machine learning tools for classification, pattern recognition, and object detection. The accelerating requirement for processing SVM yields the implementation of an SVM algorithm on the hardware. In general, the training phase for SVM is performed using software. The SVM algorithm is implemented on the hardware through the parameters generated from the training phase. Hence, training time and hardware overhead are two significant metrics to consider when improving SVM. In this paper, we propose a innovative model of SVM called Highly Parallel SVM (HPSVM) for binary classification. The HPSVM is capable of saving training time and hardware overhead while simultaneously maintaining good classification accuracy. The idea of the HPSVM is to perform the newly proposed Concurrent Gaussian Selection for picking significant training data to learn an ensemble of linear classifiers for approximation of the complicated classifier. By doing so, training time and hardware cost can be tremendously reduced. The experimental results show that, compared to the proposed parallel SVM, Ensemble of Exemplar-SVMs, the HPSVM achieves 3x training time reduction and reduces hardware cost by about 6x while slightly improving the classification accuracy.

Original languageEnglish (US)
Title of host publication2018 9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2018
EditorsSatyajit Chakrabarti, Himadri Nath Saha
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages673-682
Number of pages10
ISBN (Electronic)9781538676936
DOIs
StatePublished - Nov 2018
Event9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2018 - New York City, United States
Duration: Nov 8 2018Nov 10 2018

Publication series

Name2018 9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2018

Conference

Conference9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2018
CountryUnited States
CityNew York City
Period11/8/1811/10/18

Keywords

  • Concurrent Gaussian Selection
  • Highly Parallel SVM
  • Training data selection

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    Qin, Y., Li, B., & Lilja, D. J. (2018). Enhancing the Ensemble of Exemplar-SVMs for Binary Classification Using Concurrent Selection and Ensemble Learning. In S. Chakrabarti, & H. N. Saha (Eds.), 2018 9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2018 (pp. 673-682). [8796693] (2018 9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UEMCON.2018.8796693