Volume 5 Number 12 (Dec. 2010)
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JSW 2010 Vol.5(12): 1334-1341 ISSN: 1796-217X
doi: 10.4304/jsw.5.12.1334-1341

An Automated Error Detection for News Webpages of Chinese Portal

Deng-Yiv Chiu1, Chi-Chung Lee1, Ya-Chen Pan2
1Chung Hua University / Department of Information Management, Hsin-Chu, Taiwan, ROC
2Yuanpei University / Department of Information Management, Hsin-Chu, Taiwan, ROC

Abstract—There exists some news obviously classified into incorrect categories on Chinese webpage portals. This phenomenon is owing mainly the difficulty in automatically classifying Chinese news and the fact that news appearing on webpage portals is retrieved from numerous media sources. This study integrates genetic algorithms and multiclass support vector machine classifiers to construct an automated classification error detection approach for Chinese news classification. A genetic algorithm is utilized to select four feature thresholds used to obtain representative features/words of each class. The multi-class SVM classifier is then trained to construct an appropriate classifier to aid automated classification error detection. The experiment applies the proposed method to the Chinese news on Taiwan Yahoo!

Index Terms—multi-class support vector machine, genetic algorithm, news classification error detection.


Cite: Deng-Yiv Chiu, Chi-Chung Lee, Ya-Chen Pan, "An Automated Error Detection for News Webpages of Chinese Portal," Journal of Software vol. 5, no. 12, pp. 1334-1341, 2010.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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