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.

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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:	Completion
Status:	Unpublished
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Related URLs:	Organisation
Date of First Compliant Deposit:	6 July 2018
Last Modified:	15 Oct 2020 01:33
URI:	http://orca.cf.ac.uk/id/eprint/112927

Available Versions of this Item

• Improving imbalanced question classification using structured smote based approach. (deposited 06 Jul 2018 10:04) [Currently Displayed]
  • Improving imbalanced question classification using structured smote based approach. (deposited 15 May 2019 10:00)

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