Abstract
Generally, when developing classification models using supervised learning methods (e.g., support vector machine, neural network, and decision tree), feature selection, as a pre-processing step, is essential to reduce calculation costs and improve the generalization scores. In this regard, the minimum reference set (MRS), which is a feature selection algorithm, can be used. The original MRS considers a feature subset as effective if it leads to the correct classification of all samples by using the 1-nearest neighbor algorithm based on small samples. However, the original MRS is only applicable to numerical features, and the distances between different classes cannot be considered. Therefore, herein, we propose a novel feature subset evaluation algorithm, referred to as the “E2H distance-weighted MRS,” which can be used for a mixture of numerical and categorical features and considers the distances between different classes in the evaluation. Moreover, a Bayesian swap feature selection algorithm, which is used to identify an effective feature subset, is also proposed. The effectiveness of the proposed methods is verified based on experiments conducted using artificially generated data comprising a mixture of numerical and categorical features.