Feature Selection Approach Based on Stacked Density Granulation With Principle of Justifiable Granularity.

Abstract

Information granularity provides a framework for machine intelligence to simulate human cognitive processes in problem-solving, enabling machines to make more flexible and adaptive decisions in complex data environments. However, most existing information granularities are based on fuzzy c-mean (FCM) or K-means clustering for granularity construction. This kind of convex data structure clustering has difficulty in recognizing some irregular nonconvex data types, and can only be filled irregularly by increasing the cluster classes, which is easy to lose the wholeness and accuracy of the data. The density-based spatial clustering of applications with noise (DBSCAN), a classical density-based spatial clustering method, is well-suited for both convex and nonconvex datasets. Building on the versatility of DBSCAN, a new density-based clustering method called stacked density granulation (SDG) is proposed, which constructs information granularities capable of effectively describing both convex and nonconvex data. This approach addresses the limitations of traditional information granularity. By applying the newly constructed information granularities to the feature space, the importance of features can be measured. To enhance this process, a heuristic feature selection method called density granular feature selection (DGFS) is introduced. DGFS constructs a low-dimensional feature space by aggregating and discretizing the information granularities, retaining only the most relevant features. To demonstrate the superiority and effectiveness of the DGFS algorithm, 12 publicly available datasets are utilized, and its performance is compared with other feature selection methods across four different classifiers. The experimental results and the statistical significance test indicate that DGFS consistently outperforms the competing methods.