Classical Main Program Code for Neighborhood Rough Sets by Professor Hu Qinghua at Tianjin University

Neighborhood rough sets represent a significant extension of rough set theory, proposed by Professor Hu Qinghua's research team. This theory introduces neighborhood systems to process continuous data, overcoming the limitation of traditional rough sets that could only handle discrete data.

The core concept involves replacing equivalence relations with neighborhood relations, approximating target concepts by defining neighborhood granulation spaces. The main program typically consists of several key modules: neighborhood radius calculation, upper and lower approximation generation, attribute reduction, and dependency degree measurement.

In implementation, the algorithm first constructs a neighborhood relation matrix for data objects, which requires consideration of different distance measurement methods (such as Euclidean or Manhattan distance). The program then calculates the importance degree of each attribute, progressively selecting optimal feature subsets through heuristic algorithms like forward selection or backward elimination. The uncertainty measurement module evaluates the approximation accuracy of neighborhood upper and lower approximations, serving as a crucial indicator for assessing feature subset quality through metrics like approximation accuracy and dependency degree.

This algorithm finds wide applications in feature selection and data dimensionality reduction, particularly suitable for handling mixed-type data. Compared to traditional methods, neighborhood rough sets better preserve the geometric structure and topological characteristics of data, with implementation typically involving neighborhood particle generation functions and attribute significance evaluation routines.