Interactive streaming feature selection based on neighborhood rough sets

Abstract

Feature streams refer to features that arrive continuously over time without changing the number of samples. Such data is commonly encountered in various practical application scenarios. Stream feature selection is a technique designed to select relevant features from high-dimensional stream data, thereby reducing its overall size. Feature interaction plays a crucial role in influencing the results of feature selection. Most existing methods address stream feature selection primarily by focusing on irrelevance and redundancy, often overlooking the important interactions between features. Additionally, these methods typically assume that all samples and features are known, which contradicts the fundamental nature of streaming data. This study introduces an interactive feature selection approach for stream feature selection, utilizing the neighborhood rough set. First, we provide a basic explanation of multi-neighbor entropy, which measures the amount of information related to neighborhood classes. It is used to measure how the amount of information about neighborhood classes. Next, we propose a feature evaluation method based on correlation, redundancy, and interaction analysis. Finally, we elaborate on functions for feature evaluation criteria, aiming to design streaming feature selection algorithms that integrate correlation, redundancy, and interactivity. The proposed algorithm is compared with six other representative feature selection algorithms across 14 public datasets. Experimental results demonstrate the validity of our proposed solution.