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      • Department of Computer Engineering
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      Ensemble pruning for text categorization based on data partitioning

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      Author
      Toraman, Çağrı
      Can, Fazlı
      Date
      2011
      Source Title
      Information Retrieval Technology
      Print ISSN
      0302-9743
      Publisher
      Springer, Berlin, Heidelberg
      Volume
      7097
      Pages
      352 - 361
      Language
      English
      Type
      Conference Paper
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      Abstract
      Ensemble methods can improve the effectiveness in text categorization. Due to computation cost of ensemble approaches there is a need for pruning ensembles. In this work we study ensemble pruning based on data partitioning. We use a ranked-based pruning approach. For this purpose base classifiers are ranked and pruned according to their accuracies in a separate validation set. We employ four data partitioning methods with four machine learning categorization algorithms. We mainly aim to examine ensemble pruning in text categorization. We conduct experiments on two text collections: Reuters-21578 and BilCat-TRT. We show that we can prune 90% of ensemble members with almost no decrease in accuracy. We demonstrate that it is possible to increase accuracy of traditional ensembling with ensemble pruning. © 2011 Springer-Verlag Berlin Heidelberg.
      Keywords
      Data partitioning
      Base classifiers
      Computation costs
      Data partitioning
      Data-partitioning method
      Ensemble members
      Ensemble methods
      Ensemble pruning
      Reuters-21578
      Text categorization
      Text collection
      Data handling
      Infrared devices
      Text processing
      Information retrieval
      Permalink
      http://hdl.handle.net/11693/28247
      Published Version (Please cite this version)
      http://dx.doi.org/10.1007/978-3-642-25631-8_32
      https://doi.org/10.1007/978-3-642-25631-8
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      • Department of Computer Engineering 1368
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