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arXiv · 1810.09828

DCSVM: Fast Multi-class Classification using Support Vector Machines

Duleep Rathgamage Don·Ionut E. Iacob
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Abstract

We present DCSVM, an efficient algorithm for multi-class classification using Support Vector Machines. DCSVM is a divide and conquer algorithm which relies on data sparsity in high dimensional space and performs a smart partitioning of the whole training data set into disjoint subsets that are easily separable. A single prediction performed between two partitions eliminates at once one or more classes in one partition, leaving only a reduced number of candidate classes for subsequent steps. The algorithm continues recursively, reducing the number of classes at each step, until a final binary decision is made between the last two classes left in the competition. In the best case scenario, our algorithm makes a final decision between $k$ classes in $O(\log k)$ decision steps and in the worst case scenario DCSVM makes a final decision in $k-1$ steps, which is not worse than the existent techniques.

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Duleep Rathgamage Don, Ionut E. Iacob. 2018-10-23. DCSVM: Fast Multi-class Classification using Support Vector Machines. https://arxiv.org/abs/1810.09828

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