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WEC: weighted ensemble of text classifiers.

Upadhyay, Ashish; Nguyen, Tien Thanh; Massie, Stewart; McCall, John

Authors

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Professor John McCall j.mccall@rgu.ac.uk
Professorial Lead (NSC) & Lead of the Computational Intelligence Research Group



Abstract

Text classification is one of the most important tasks in the field of Natural Language Processing. There are many approaches that focus on two main aspects: generating an effective representation; and selecting and refining algorithms to build the classification model. Traditional machine learning methods represent documents in vector space using features such as term frequencies, which have limitations in handling the order and semantics of words. Meanwhile, although achieving many successes, deep learning classifiers require substantial resources in terms of labelled data and computational complexity. In this work, a weighted ensemble of classifiers (WEC) is introduced to address the text classification problem. Instead of using majority vote as the combining method, we propose to associate each classifier’s prediction with a different weight when combining classifiers. The optimal weights are obtained by minimising a loss function on the training data with the Particle Swarm Optimisation algorithm. We conducted experiments on 5 popular datasets and report classification performance of algorithms with classification accuracy and macro F1 score. WEC was run with several different combinations of traditional machine learning and deep learning classifiers to show its flexibility and robustness. Experimental results confirm the advantage of WEC, especially on smaller datasets.

Citation

UPADHYAY, A., NGUYEN, T.T., MASSIE, S. and MCCALL, J. 2020. WEC: weighted ensemble of text classifiers. In Proceedings of 2020 Institute of Electrical and Electronics Engineers (IEEE) congress on evolutionary computation (IEEE CEC 2020), part of the 2020 (IEEE) World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 International joint conference on neural networks (IJCNN 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, Glasgow, UK [virtual conference]. Piscataway: IEEE [online], article ID 9185641. Available from: https://doi.org/10.1109/CEC48606.2020.9185641

Conference Name 2020 Institute of Electrical and Electronics Engineers (IEEE) congress on evolutionary computation (IEEE CEC 2020), part of the 2020 (IEEE) World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 International joint conf
Conference Location Glasgow, UK [virtual conference]
Start Date Jul 19, 2020
End Date Jul 24, 2020
Acceptance Date Mar 20, 2020
Online Publication Date Jul 24, 2020
Publication Date Sep 3, 2020
Deposit Date May 14, 2020
Publicly Available Date May 14, 2020
Publisher Institute of Electrical and Electronics Engineers
DOI https://doi.org/10.1109/CEC48606.2020.9185641
Keywords Text classification; Ensemble method; Ensemble of classifiers; Multiple classifiers; Particle swarm optimisation
Public URL https://rgu-repository.worktribe.com/output/905942

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