Intelligent partitioning for feature selection

This paper develops a new optimization-based feature-selection framework for knowledge discovery in databases. Algorithms following this new framework have attractive theoretical properties such as proven convergence to an optimal set of relevant features and the ability for deriving rigorous statements regarding the quality of the set that is found. Within this framework both wrapper and filter algorithms are derived, and numerical experiments show the new methodology to perform well with respect to accuracy and simplicity of the set of features found to be relevant.