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Parallelization of Multi-label classification for large data sets

Biswas, S and Susheela Devi, V (2019) Parallelization of Multi-label classification for large data sets. In: UNSPECIFIED, pp. 2005-2010.

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Official URL: https://dx.doi.org/10.1109/SSCI.2018.8628763


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) 1 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 five benchmark multi-label data sets, in terms of both accuracy and speedup of the process. © 2018 IEEE.

Item Type: Conference Paper
Publication: Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: Copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords: Artificial intelligence; Data mining; Learning algorithms, Feature selection methods; Growing demand; Map-reduce; Multi label classification; Multi-label learning; Parallel processing; Parallelizations; Redundant features, Classification (of information)
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 15 Apr 2019 05:17
Last Modified: 15 Apr 2019 05:17
URI: http://eprints.iisc.ac.in/id/eprint/62090

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