Squeezing the ensemble pruning: Faster and more accurate categorization for news portals

Date

2012-04-27

Author

Toraman, Çağrı
Can, Fazli

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Recent studies show that ensemble pruning works as effective as traditional ensemble of classifiers (EoC). In this study, we analyze how ensemble pruning can improve text categorization efficiency in time-critical real-life applications such as news portals. The most crucial two phases of text categorization are training classifiers and assigning labels to new documents; but the latter is more important for efficiency of such applications. We conduct experiments on ensemble pruning-based news article categorization to measure its accuracy and time cost. The results show that our heuristics reduce the time cost of the second phase. Also we can make a trade-off between accuracy and time cost to improve both of them with appropriate pruning degrees. © 2012 Springer-Verlag Berlin Heidelberg.

Subject Keywords

Ensemble pruning, news portal, text categorization

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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84860148644&origin=inward
https://hdl.handle.net/11511/109656

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Department of Computer Engineering, Conference / Seminar