Balanced bootstrap resampling method for neural model selection

Authors:
Wen-Liang Hung;E. Stanley Lee;Shun-Chin Chuang
Affiliations:
Department of Applied Mathematics, National Hsinchu University of Education, Hsin-Chu, 30013, Taiwan;Department of Industrial and Manufacturing Systems Engineering, Kansas State University, KS 66506, USA;Headquarters Administration of Cultural Heritage, Council for Cultural Affairs, Tai-Chung, 40247, Taiwan
Venue:
Computers & Mathematics with Applications
Year:
2011

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Abstract

Uniform resampling is the easiest to apply and is a general recipe for all problems, but it may require a large replication size B. To save computational effort in uniform resampling, balanced bootstrap resampling is proposed to change the bootstrap resampling plan. This resampling plan is effective for approximating the center of the bootstrap distribution. Therefore, this paper applies it to neural model selection. Numerical experiments indicate that it is possible to considerably reduce the replication size B. Moreover, the efficiency of balanced bootstrap resampling is also discussed in this paper.