Exploiting Heterogeneous Features for Classification Learning

摘要: This paper proposes a framework for handling heterogeneous features containing hierarchical values and texts under Bayesian learning. To exploit features, we make use of statistical technique called shrinkage. We also explore an approach utilizing text data to improve classification performance. have evaluated our using yeast gene set which contain as well data.