| Issue |
E3S Web Conf.
Volume 491, 2024
International Conference on Environmental Development Using Computer Science (ICECS’24)
|
|
|---|---|---|
| Article Number | 01014 | |
| Number of page(s) | 9 | |
| Section | Energy Management for Sustainable Environment | |
| DOI | https://doi.org/10.1051/e3sconf/202449101014 | |
| Published online | 21 February 2024 | |
One-vs-Rest vs. Voting Classifiers for Multi-Label Text Classification: An Empirical Study
Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Potheri, Kattankulathur – 603203
* Corresponding author: pradeeps1@srmist.edu.in
Inthispaper,weconductanempiricalstudytocompare the performance of two popular approaches for multi-label text classification, which is a challenging task in naturallanguageprocessingthatrequirespredictingmultiplelabelsforagiventext:One-vs-RestandVotingclassifiers.Weevaluatetheseclassifiersonadatasetoftoxiccommentsandmeasuretheir performance using accuracy and hamming loss evaluationmetrics.OurexperimentalresultsshowthattheOne-vs-RestclassifierwithXGBoutperformstheVotingclassifierandachievesanaccuracyof91.7%.Thestudy’sresultscanbeusedasabenchmarkforfutureresearchinthisarea,andtheinsightsgained can be used to improve the accuracy and robustness ofmulti-label text classification models. Furthermore, our findingssuggest that the One-vs-Rest classifier with XGB is a promisingapproachformulti-labeltextclassificationtasks,whichcanprovidebetterresultsthanotherpopularclassifiers
© The Authors, published by EDP Sciences, 2024
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