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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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 |
|---|---|
| Publisher: | IEEE |
| 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 |
| 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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