Feature selection is one of the important topics in machine learning and even artificial intelligence. Inaddition, feature selection using neighborhood rough sets has been proven to be an effective way. However, the sensitivity of most existing algorithms to imbalanced data is an important flaw in practical applications. This paper discusses how to use neighborhood rough sets to solve the problem of feature extraction when the distribution of heterogeneous data is unbalanced.Because the distribution of real-world data is not always uniform, feature selection algorithms can be applied in a wider range of fields, such as fraud identification, recommendation systems, etc. The algorithm for research on unbalanced data in this paper can enable researchers and even industry professionals to obtainmore effective results when dealing with problems in practical applications.

Neighborhood rough sets are now widely used to process numerical data. Nevertheless, most of the existing neighborhood rough sets are not able to distinguish class mixture samples well when dealing with classification problems. That is, it cannot effectively classify categories when dealing with data with an unbalanced distribution. Because of this, in this article, we propose a new feature selection method that takes into consideration both heterogeneous data and feature interaction. The proposed model well integrates the ascendancy of ${\delta }$-neighborhood and ${k}$-nearest neighbor. Such heterogeneous data can be handled better than existing neighborhood models. We utilize information entropy theories such as mutual information and conditional mutual information and employ an iterative strategy to define the importance of each feature in decision making. Furthermore, we design a feature extraction algorithm based on the above idea. Experimental results display that the raised alg...