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

Abstract

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.

Sensor data reduction with novel local neighborhood information granularity and rough set approach

“…To deal with these sensing data, data description and data reduction are pivotal process. For data acquisition, rough sets based models are considered as effective approaches in recent years [1,2]. Rough set theory [3] was proposed in 1982 by Pawlak as a mathematical tool for analyzing and handling imprecise, inconsistent, and incomplete information.…”