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

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

Khin Sandar Kyaw, Somchai Limsiroratana
Copyright: © 2020 |Volume: 11 |Issue: 4 |Pages: 23
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781799806721|DOI: 10.4018/IJSIR.2020100101
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MLA

Kyaw, Khin Sandar, and Somchai Limsiroratana. "Optimization of Text Feature Selection Process Based on Advanced Searching for News Classification." IJSIR vol.11, no.4 2020: pp.1-23. http://doi.org/10.4018/IJSIR.2020100101

APA

Kyaw, K. S. & Limsiroratana, S. (2020). Optimization of Text Feature Selection Process Based on Advanced Searching for News Classification. International Journal of Swarm Intelligence Research (IJSIR), 11(4), 1-23. http://doi.org/10.4018/IJSIR.2020100101

Chicago

Kyaw, Khin Sandar, and Somchai Limsiroratana. "Optimization of Text Feature Selection Process Based on Advanced Searching for News Classification," International Journal of Swarm Intelligence Research (IJSIR) 11, no.4: 1-23. http://doi.org/10.4018/IJSIR.2020100101

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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.

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