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Vol.19 No.4
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Automatic Text Categorization Using Hybrid Multiple Model Schemes

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2002, v.19 no.4, pp.35-51
https://doi.org/10.3743/KOSIM.2002.19.4.035


& (2002). Automatic Text Categorization Using Hybrid Multiple Model Schemes. Journal of the Korean Society for Information Management, 19(4), 35-51, https://doi.org/10.3743/KOSIM.2002.19.4.035
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Abstract

Inductive learning and classification techniques have been employed in various research and applications that organize textual data to solve the problem of information access. In this study, we develop hybrid model combination methods which incorporate the concepts and techniques for multiple modeling algorithms to improve the accuracy of text classification, and conduct experiments to evaluate the performances of proposed schemes. Boosted stacking, one of the extended stacking schemes proposed in this study yields higher accuracy relative to the conventional model combination methods and single classifiers.

keywords
hybrid multiple model, text classification, multiple modeling algorithm, 문서분류, 기계학습, 다중모델 학습

Journal of the Korean Society for Information Management

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