Optimization of Text Feature Selection Process Based on Advanced Searching for News Classification

Khin Sandar Kyaw and Somchai Limsiroratana
Additional contact information
Khin Sandar Kyaw: Department of Computer Engineering, Prince of Songkla University, Thailand
Somchai Limsiroratana: Department of Computer Engineering, Prince of Songkla University, Thailand

International Journal of Swarm Intelligence Research (IJSIR), 2020, vol. 11, issue 4, 1-23

Abstract: Nowadays, the culture for accessing news around the world is changed from paper format to electronic and the rate of publication for newspapers and magazines on websites have increased dramatically. Therefore, the feature selection process from high-dimensional text feature set for an automatic news classification model is becoming the top challenge because irrelevant features can degrade the accuracy with high cost computation time for classification model. In this article, six-advanced search policies based on evolutionary, swarm intelligence, nature-inspired intelligence are observed for achieving the global optimal feature subset for optimal accuracy in news classification problem. According to the experimental results, the advanced search schemes that can provide flexibility in integrating classifier in accordance with its objective function such as optimal classification performance by adjusting the rate of modification parameters for the testing data.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJSIR.2020100101 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jsir00:v:11:y:2020:i:4:p:1-23

Access Statistics for this article

International Journal of Swarm Intelligence Research (IJSIR) is currently edited by Yuhui Shi

More articles in International Journal of Swarm Intelligence Research (IJSIR) from IGI Global
Bibliographic data for series maintained by Journal Editor ().