  Feature selection is an important task in many problems occurring in pattern
recognition, bioinformatics, machine learning and data mining applications. The
feature selection approach enables us to reduce the computation burden and the
falling accuracy effect of dealing with huge number of features in typical
learning problems. There is a variety of techniques for feature selection in
supervised learning problems based on different selection metrics. In this
paper, we propose a novel unified framework for feature selection built on the
graphical models and information theoretic tools. The proposed approach
exploits the structure learning among features to select more relevant and less
redundant features to the predictive modeling problem according to a primary
novel likelihood based criterion. In line with the selection of the optimal
subset of features through the proposed method, it provides us the Bayesian
network classifier without the additional cost of model training on the
selected subset of features. The optimal properties of our method are
established through empirical studies and computational complexity analysis.
Furthermore the proposed approach is evaluated on a bunch of benchmark datasets
based on the well-known classification algorithms. Extensive experiments
confirm the significant improvement of the proposed approach compared to the
earlier works.
