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Parallelization of Multi-label classification for large data sets

Biswas, Shinjini and Devi, Susheela V (2018) Parallelization of Multi-label classification for large data sets. In: 2018 IEEE Symposium Series On Computational Intelligence (IEEE SSCI), NOV 18-21, 2018, Bengaluru, INDIA, pp. 2005-2010.

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Abstract

Over the last few years, multi-label learning has received a lot of attention in research and industries. Since a pattern can belong to more than one class at the same time, it is a very challenging task to classify a test pattern. Multi-label classification algorithms while inferring on large data sets take a long time to run. So, there is a growing demand of an effective and efficient method for multi-label classification problems, both in terms of accuracy and speed. We endeavour to improve the performance and accuracy of a multi-label classification algorithm which, given a pattern, can predict the set of labels it belongs to, for large data sets, using parallel computing in a distributed manner. We also reduced the dimensionality of large data sets with very large number of features by removing the redundant features using a feature selection method (Fscore) to improve the accuracy and reduce the time taken for training phase of the multi-label classification algorithm. The result shows the benefits of using parallel processing over the traditional single-node execution, tested over live benchmark multi-label data sets, in terms of both accuracy and speedup of the process.

Item Type: Conference Proceedings
Additional Information: 8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI), Bengaluru, INDIA, NOV 18-21, 2018
Keywords: Multi-label classification; parallelization; MapReduce
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Id for Latest eprints
Date Deposited: 15 Mar 2019 05:08
Last Modified: 15 Mar 2019 05:08
URI: http://eprints.iisc.ac.in/id/eprint/61956

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