Boosting feature selection

D. B. Redpath, K. Lebart

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

24 Citations (Scopus)


It is possible to reduce the error rate of a single classifier using a classifier ensemble. However, any gain in performance is undermined by the increased computation of performing classification several times. Here the AdaboostFS algorithm is proposed which builds on two popular areas of ensemble research: Adaboost and Ensemble Feature Selection (EFS). The aim of AdaboostFSs is to reduce the number of features used by each base classifer and hence the overall computation required by the ensemble. To do this the algorithm combines a regularised version of Boosting AdaboostReg [1] with a floating feature search for each base classifier. Adaboost FS is compared using four benchmark data sets to Adaboost All, which uses all features and to AdaboostRSM, which uses a random selection of features. Performance is assessed based on error rate, ensemble error and diversity, and the total number of features used for classification. Results show that AdaboostFS achieves a lower error rate and higher diversity than AdaboostAll, and achieves a lower error rate and comparable diversity to AdaboostRSM However, over the other methods AdaboostFS produces a significant reduction in the number of features required for classification in each base classifier and the entire ensemble. © Springer-Verlag Berlin Heidelberg 2005.

Original languageEnglish
Title of host publicationPattern Recognition and Data Mining
Subtitle of host publicationThird International Conference on Advances in Pattern Recognition, ICAPR 2005, Bath, UK, August 22-25, 2005, Proceedings, Part I
Number of pages10
ISBN (Electronic)978-3-540-28758-2
Publication statusPublished - 2005
EventThird International Conference on Advances in Patten Recognition - Bath, United Kingdom
Duration: 22 Aug 200525 Aug 2005

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


ConferenceThird International Conference on Advances in Patten Recognition
Abbreviated titleICAPR 2005
Country/TerritoryUnited Kingdom


Dive into the research topics of 'Boosting feature selection'. Together they form a unique fingerprint.

Cite this