Exploiting Hierarchical Domain Values for Bayesian Learning

Yiqiu Han, Wai Lam

Abstract: This paper proposes a framework for exploiting hierarchical structures of feature domain values in order to improve classification performance under Bayesian learning framework. Inspired by the statistical technique called shrinkage, we investigate the variances in the estimation of parameters for Bayesian learning. We develop two algorithms by maintaining a balance between precision and robustness to improve the estimation. We have evaluated our methods using two real-world data sets, namely, a weather data set and a yeast gene data set. The results demonstrate that our models benefit from exploring the hierarchical structures.

External IDs:dblp:conf/pakdd/HanL03