Automatic Document Classification Using Multiple Classifier Systems 


Vol. 11,  No. 5, pp. 545-554, Aug.  2004
10.3745/KIPSTB.2004.11.5.545


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  Abstract

Combining multiple classifiers to obtain improved performance over the individual classifier has been a widely used technique. The task of constructing a multiple classifier system(MCS) contains two different issues : how to generate a diverse set of base-level classifiers and how to combine their predictions. In this paper, we review the characteristics of existing multiple classifier systems : Bagging, Boosting, and Stacking. For document classification, we propose new MCSs such as Stacked Bagging, Stacked Boosting, Bagged Stacking, Boosted Stacking. These MCSs are a sort of hybrid MCSs that combine advantages of existing MCSs such as Bagging, Boosting, and Stacking. We conducted some experiments of document classification to evaluate the performances of the proposed schemes on MEDLINE, Usenet news, and Web document collections. The result of experiments demonstrate the superiority of our hybrid MCSs over the existing ones.

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[IEEE Style]

I. C. Kim, "Automatic Document Classification Using Multiple Classifier Systems," The KIPS Transactions:PartB , vol. 11, no. 5, pp. 545-554, 2004. DOI: 10.3745/KIPSTB.2004.11.5.545.

[ACM Style]

In Cheol Kim. 2004. Automatic Document Classification Using Multiple Classifier Systems. The KIPS Transactions:PartB , 11, 5, (2004), 545-554. DOI: 10.3745/KIPSTB.2004.11.5.545.

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