Improving imbalanced question classification using structured smote based approach

Alaa Mohasseb; Mohamed Bader-El-Den; Mihaela Cocea; Han Liu

doi:10.1109/ICMLC.2018.8527028

Improving imbalanced question classification using structured smote based approach

Alaa Mohasseb, Mohamed Bader-El-Den, Mihaela Cocea, Han Liu

School of Computing

Cardiff University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

164 Downloads (Pure)

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.

Original language	English
Title of host publication	Proceedings of the 2018 International Conference on Machine Learning and Cybernetics (ICMLC)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	593-597
Number of pages	6
Volume	2
ISBN (Electronic)	978-1-5386-5214-5
ISBN (Print)	978-1-5386-5215-2
DOIs	https://doi.org/10.1109/ICMLC.2018.8527028
Publication status	Published - 12 Nov 2018
Event	2018 International Conference on Machine Learning and Cybernetics - http://www.icmlc.com/icmlc/welcome.html, Chengdu, China Duration: 15 Jul 2018 → 18 Jul 2018

Publication series

Name	International Conference on Machine Learning and Cybernetics (ICMLC)
Publisher	IEEE
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	2018 International Conference on Machine Learning and Cybernetics
Abbreviated title	ICMLC 2018
Country/Territory	China
City	Chengdu
Period	15/07/18 → 18/07/18

Keywords

Information Retrieval
Text classification
Question classification
Machine Learning
Class Imbalance

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10.1109/ICMLC.2018.8527028

Class_Imbalance___ALAA
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Accepted author manuscript (Post-print), 181 KB

Cite this

Mohasseb, A., Bader-El-Den, M., Cocea, M., & Liu, H. (2018). Improving imbalanced question classification using structured smote based approach. In Proceedings of the 2018 International Conference on Machine Learning and Cybernetics (ICMLC) (Vol. 2, pp. 593-597). (International Conference on Machine Learning and Cybernetics (ICMLC)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLC.2018.8527028

Mohasseb, Alaa ; Bader-El-Den, Mohamed ; Cocea, Mihaela et al. / Improving imbalanced question classification using structured smote based approach. Proceedings of the 2018 International Conference on Machine Learning and Cybernetics (ICMLC). Vol. 2 Institute of Electrical and Electronics Engineers Inc., 2018. pp. 593-597 (International Conference on Machine Learning and Cybernetics (ICMLC)).

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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.",

keywords = "Information Retrieval, Text classification, Question classification, Machine Learning, Class Imbalance",

author = "Alaa Mohasseb and Mohamed Bader-El-Den and Mihaela Cocea and Han Liu",

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Mohasseb, A , Bader-El-Den, M , Cocea, M & Liu, H 2018, Improving imbalanced question classification using structured smote based approach. in Proceedings of the 2018 International Conference on Machine Learning and Cybernetics (ICMLC). vol. 2, International Conference on Machine Learning and Cybernetics (ICMLC), Institute of Electrical and Electronics Engineers Inc., pp. 593-597, 2018 International Conference on Machine Learning and Cybernetics, Chengdu, China, 15/07/18. https://doi.org/10.1109/ICMLC.2018.8527028

Improving imbalanced question classification using structured smote based approach. / Mohasseb, Alaa ; Bader-El-Den, Mohamed ; Cocea, Mihaela et al.
Proceedings of the 2018 International Conference on Machine Learning and Cybernetics (ICMLC). Vol. 2 Institute of Electrical and Electronics Engineers Inc., 2018. p. 593-597 (International Conference on Machine Learning and Cybernetics (ICMLC)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Bader-El-Den, Mohamed

AU - Cocea, Mihaela

AU - Liu, Han

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N2 - 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.

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KW - Text classification

KW - Question classification

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Mohasseb A , Bader-El-Den M , Cocea M, Liu H. Improving imbalanced question classification using structured smote based approach. In Proceedings of the 2018 International Conference on Machine Learning and Cybernetics (ICMLC). Vol. 2. Institute of Electrical and Electronics Engineers Inc. 2018. p. 593-597. (International Conference on Machine Learning and Cybernetics (ICMLC)). doi: 10.1109/ICMLC.2018.8527028

Improving imbalanced question classification using structured smote based approach

Abstract

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Keywords

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