On Designing Pattern Classifiers Using Artificially Created Bootstrap Samples

Qun Wang, B. John Oommen

Abstract

We consider the problem of building a pattern recognition classifier using a set of training samples. Traditionally, the classifier is constructed by using only the set1 of given training samples. But the quality of the classifier is poor when the size of the training sample is small. In this paper, we shall show that the quality of the classifier can be improved by utilizing artificially created training samples, where the latter are obtained by using various extensions of Efron’s bootstrap technique. Experimental results show that classifiers which incorporate some of the bootstrap algorithms, noticeably improve the performance of the resultant classifier.

References

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Paper Citation


in Harvard Style

Wang Q. and John Oommen B. (2004). On Designing Pattern Classifiers Using Artificially Created Bootstrap Samples . In Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2004) ISBN 972-8865-01-5, pages 159-168. DOI: 10.5220/0002653901590168


in Bibtex Style

@conference{pris04,
author={Qun Wang and B. John Oommen},
title={On Designing Pattern Classifiers Using Artificially Created Bootstrap Samples},
booktitle={Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2004)},
year={2004},
pages={159-168},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002653901590168},
isbn={972-8865-01-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2004)
TI - On Designing Pattern Classifiers Using Artificially Created Bootstrap Samples
SN - 972-8865-01-5
AU - Wang Q.
AU - John Oommen B.
PY - 2004
SP - 159
EP - 168
DO - 10.5220/0002653901590168