iBoost: Boosting Using an I Nstance-Based Exponential Weighting Scheme
Abstract
Recently, Freund, Mansour and Schapire established that using exponential weighting scheme in combining classifiers reduces the problem of overfitting. Also, Helmbold, Kwek and Pitt that showed in the prediction using a pool of experts framework an instance based weighting scheme improves performance. Motivated by these results, we propose here an instance-based exponential weighting scheme in which the weights of the base classifiers are adjusted according to the test instance x . Here, a competency classifier ci is constructed for each base classifier hi to predict whether the base classifier’s guess of x ’s label can be trusted and adjust the weight of hi accordingly. We show that this instance-based exponential weighting scheme enhances the performance of AdaBoost.
Cite
Text
Kwek and Nguyen. "iBoost: Boosting Using an I Nstance-Based Exponential Weighting Scheme." European Conference on Machine Learning, 2002. doi:10.1007/3-540-36755-1_21Markdown
[Kwek and Nguyen. "iBoost: Boosting Using an I Nstance-Based Exponential Weighting Scheme." European Conference on Machine Learning, 2002.](https://mlanthology.org/ecmlpkdd/2002/kwek2002ecml-iboost/) doi:10.1007/3-540-36755-1_21BibTeX
@inproceedings{kwek2002ecml-iboost,
title = {{iBoost: Boosting Using an I Nstance-Based Exponential Weighting Scheme}},
author = {Kwek, Stephen and Nguyen, Chau},
booktitle = {European Conference on Machine Learning},
year = {2002},
pages = {245-257},
doi = {10.1007/3-540-36755-1_21},
url = {https://mlanthology.org/ecmlpkdd/2002/kwek2002ecml-iboost/}
}