Balanced bootstrap resampling method for neural model selection

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.