Glee: A granularity filter for feature selection

Abstract

In the field of Granular Computing (GrC), feature selection is an attractive task. Some basics of GrC such as information granulation and granularity have well guided the explorations of feature selection technique. Nevertheless, some challenges to perform feature selection such as high computational costs of repeating information granulation and multi-granularity structure in the process of searching qualified features may also arise. In this study, to improve the efficiency and effectiveness of feature selector, a novel framework named Glee: Granularity fiLter for fEature sElection, is reported. Firstly, the value of granularity related to each feature is calculated. Secondly, all features are reordered by the corresponding values of granularity. Finally, following such a derived sequence, features will be added into the selection pool one by one until the termination condition is achieved. Glee can not only eliminate the iterative calculations of information granulation in the whole process of selecting, but also provide sequence of features which may be insensitive to data perturbation. It should also be emphasized that Glee is a general framework, and most existing termination conditions to feature selection can be embedded into it. To validate the effectiveness of Glee, it is compared with several well-established feature selection schemes in elapsed time of selecting features, stability of selected features and classification performance. Experimental results over 20 UCI datasets with both raw features and 3 different ratios of noisy features demonstrate that our framework is superior as it yields better robustness with satisfactory elapsed time.