A Novel Approach to Distributed Multi-Class SVM


  • Aruna Govada BITS Pilani K K Birla Goa Campus
  • Shree S S. Ranjani BITS Pilani K K Birla Goa Campus
  • Aditi Viswanathan BITS Pilani K.K.Birla Goa Campus
  • S.K. Sahay




With data sizes constantly expanding, and with classical machine learning algorithms that analyze such data requiring larger and larger amounts of computation time and storage space, the need to distribute computation and memory requirements among several computers has become apparent. Although substantial work has been done in developing distributed binary SVM algorithms and multi-class SVM algorithms individually, the field of multi-class distributed SVMs remains largely unexplored. This research proposes a novel algorithm that implements the Support Vector Machine over a multi-class dataset and is efficient in a distributed environment (here, Hadoop). The idea is to divide the dataset into half recursively and thus compute the optimal Support Vector Machine for this half during the training phase, much like a divide and conquer approach. While testing, this structure has been effectively exploited to significantly reduce the prediction time. Our algorithm has shown better computation time during the prediction phase than the traditional sequential SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of the dataset grows. This approach also classifies the data with higher accuracy than the traditional multi-class algorithms.

Author Biographies

Aruna Govada, BITS Pilani K K Birla Goa Campus

Department of CS & IS

Aditi Viswanathan, BITS Pilani K.K.Birla Goa Campus

Department of CS & IS


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How to Cite

Govada, A., S S. Ranjani, S., Viswanathan, A., & Sahay, S. (2014). A Novel Approach to Distributed Multi-Class SVM. Transactions on Engineering and Computing Sciences, 2(5), 72–79. https://doi.org/10.14738/tmlai.25.562