Mohasseb, Alaa, Bader, Mohamed, Cocea, Mihaela and Liu, Han  ORCID: https://orcid.org/0000-0002-7731-8258
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.
|
Preview |
PDF
- Accepted Post-Print Version
Download (185kB) | Preview |
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| Status: | Unpublished |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: | Schools > Computer Science & Informatics |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Related URLs: | |
| Date of First Compliant Deposit: | 6 July 2018 |
| Last Modified: | 23 Oct 2022 14:09 |
| URI: | https://orca.cardiff.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]
Citation Data
Cited 6 times in Scopus. View in Scopus. Powered By Scopus® Data
Actions (repository staff only)
 |
Edit Item |
Download Statistics
Download Statistics