Feature selection via neighborhood multi-granulation fusion

Feature selection is an important data preprocessing technique, and has been widely studied in data mining, machine learning, and granular computing. However, very little research has considered a multi-granulation perspective. In this paper, we present a new feature selection method that selects distinguishing features by fusing neighborhood multi-granulation. We first use neighborhood rough sets as an effective granular computing tool, and analyze the influence of the granularity of neighborhood information. Then, we obtain all feature rank lists based on the significance of features in different granularities. Finally, we obtain a new feature selection algorithm by fusing all individual feature rank lists. Experimental results show that the proposed method can effectively select a discriminative feature subset, and performs as well as or better than other popular feature selection algorithms in terms of classification performance.