Weighted bootstrap for neural model selection

Authors:
Shun-Chin Chuang;Wen-Liang Hung;Hsin-Chia Fu
Affiliations:
Department of Computer Science and Information Engineering, National Chiao-Tung University, Hsin-Chu, Taiwan;Graduate Institute of Computer Science, National Hsinchu University of Education, Hsin-Chu, Taiwan;Department of Computer Science and Information Engineering, National Chiao-Tung University, Hsin-Chu, Taiwan
Venue:
International Journal of Systems Science
Year:
2008

Citing 2
Cited 1

Some properties of the exponential entropy

Information Sciences: an International Journal
A Similarity-Based Robust Clustering Method

IEEE Transactions on Pattern Analysis and Machine Intelligence

Balanced bootstrap resampling method for neural model selection

Computers & Mathematics with Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

This article proposes a weighted bootstrap procedure, which is an efficient bootstrap technique for neural model selection. Our primary interest in reducing computer effort is to not resample (in the original bootstrap procedure) uniformly from the original sample, but to modify this distribution in order to obtain variance reduction. The performance of the weighted bootstrap is demonstrated on two artificial data sets and one real dataset. Experimental results show that the weighted bootstrap procedure permits an approximately 2 to 1 reduction in replication size.