Feature selection using a weighted method in interval-valued decision information systems

Recent developments in big data applications have heightened the need for understanding and processing high-dimensional data. It is necessary to extract some excellent features that effect the learning performance in high-dimensional data. Feature selection algorithm based on rough set theory as an important preprocessing method has been widely used in practical applications. Meanwhile, it should be noted that different attributes have different effects on model evaluation. Nevertheless, each feature or attribute has the same degree of importance in the interval-valued information system by using rough set models, ignoring the imbalance between features. Moreover, the monotonic classification effect of interval-valued data is easily affected by noise. For these two issues, we introduce different weights into neighborhood relations and propose a novel approach for feature selection-based weighted neighborhood rough sets for interval-valued information systems in this study. First, weighted neighborhood relations and some important properties are proposed by considering different attribute weights in the interval-valued information system. Then, we construct an interval-valued-based weighted neighborhood rough set (IVWNRS) model to solve the contradiction between the degree of dependency and the classification ability of the attribute subset. Furthermore, a heuristic algorithm is designed according to the degree of dependency to select an attribute subset that has both strong correlation and high dependency. Finally, we compare it with six other representative feature selection algorithms on fifteen public datasets to evaluate the performance of the proposed algorithm. Experimental results on different classifiers show that the IVWNRS algorithm has higher classification performance and is significantly effective.