A novel feature selection method considering feature interaction in neighborhood rough set

Feature selection has been shown to be a highly valuable strategy in data mining, pattern recognition, and machine learning. However, the majority of proposed feature selection methods do not account for feature interaction while calculating feature correlations. Interactive features are those features that have less individual relevance with the class, but can provide more joint information for the class when combined with other features. Inspired by it, a novel feature selection algorithm considering feature relevance, redundancy, and interaction in neighborhood rough set is proposed. First of all, a new method of information measurement called neighborhood symmetric uncertainty is proposed, to measure what proportion data a feature contains regarding category label. Afterwards, a new objective evaluation function of the interactive selection is developed. Then a novel feature selection algorithm named (NSUNCMI) based on measuring feature correlation, redundancy and interactivity is proposed. The results on the nine universe datasets and five representative feature selection algorithms indicate that NSUNCMI reduces the dimensionality of feature space efficiently and offers the best average classification accuracy.

Automated summary

Feature selection is a valuable strategy in data mining, pattern recognition, and machine learning. However, most existing methods do not consider feature interaction when calculating correlations. This study proposes a novel feature selection algorithm that takes into account feature relevance, redundancy, and interaction. The algorithm introduces a new information measurement method called neighborhood symmetric uncertainty and develops an objective evaluation function for interactive selection. The results show that the proposed algorithm effectively reduces dimensionality and achieves the best average classification accuracy among the compared algorithms.