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Improving imbalanced question classification using structured smote based approach

Mohasseb, Alaa, Bader, Mohamed, Cocea, Mihaela and Liu, Han 2018. Improving imbalanced question classification using structured smote based approach. Presented at: International Conference on Machine Learning and Cybernetics (ICMLC 2018), Chengdu, China, 15-18 July 2018. 2018 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, pp. 593-597. 10.1109/ICMLC.2018.8527028

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Abstract

Questions Classification (QC) is one of the most popular text classification applications. QC plays an important role in question-answering systems. However, as in many real-world classification problems, QC may suffer from the problem of class imbalance. The classification of imbalanced data has been a key problem in machine learning and data mining. In this paper, we propose a framework that deals with the class imbalance using a hierarchical SMOTE algorithm for balancing different types of questions. The proposed framework is grammar-based, which involves using the grammatical pattern for each question and using machine learning algorithms to classify them. Experimental results imply that the proposed framework demonstrates a good level of accuracy in identifying different question types and handling class imbalance.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: IEEE
ISBN: 978-1-5386-5214-5
Related URLs:
Date of First Compliant Deposit: 6 July 2018
Date of Acceptance: 17 May 2018
Last Modified: 15 May 2019 10:00
URI: http://orca-mwe.cf.ac.uk/id/eprint/119818

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