Constructing Heterogeneous Committees Using Input Feature Grouping: Application to Economic Forecasting

Neural Information Processing Systems 

The committee approach has been proposed for reducing model uncertainty and improving generalization performance. The ad(cid:173) vantage of committees depends on (1) the performance of individ(cid:173) ual members and (2) the correlational structure of errors between members. This paper presents an input grouping technique for de(cid:173) signing a heterogeneous committee. With this technique, all input variables are first grouped based on their mutual information. Sta(cid:173) tistically similar variables are assigned to the same group.