Ensemble Pruning for Text Categorization Based on Data Partitioning

Date

2011-01-01

Author

Toraman, Çağrı
Can, Fazli

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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.

URI

https://hdl.handle.net/11511/109624

DOI

https://doi.org/10.1007/978-3-642-25631-8_32

Conference Name

7th Asia Information Retrieval Societies Conference (AIRS 2011)

Collections

Department of Computer Engineering, Conference / Seminar

Citation Formats

Ç. Toraman and F. Can, “Ensemble Pruning for Text Categorization Based on Data Partitioning,” presented at the 7th Asia Information Retrieval Societies Conference (AIRS 2011), Dubai, Birleşik Arap Emirlikleri, 2011, Accessed: 00, 2024. [Online]. Available: https://hdl.handle.net/11511/109624.