Utilizing hierarchical feature domain values for prediction

We propose a Bayesian learning framework which can exploit hierarchical structures of discrete feature domain values to improve the prediction performance on sparse training data. One characteristic of our framework is that it provides a principled way based on mean-variance analysis to transform an original feature domain value to a coarser granularity by exploiting the underlying hierarchical structure. Through this transformation, a tradeoff between precision and robustness is achieved to improve the parameter estimation for prediction. We have conducted comparative experiments using three real-world data sets. The results demonstrate that utilizing domain value hierarchies gains benefits for prediction.