Attribute reduction based on improved information entropy

The choice of attribute significance is the most important step of attribute reduction algorithm. Information entropy is a method of calculating the importance of attributes. Due to the fact that information view only takes the size of knowledge granularity into account rather than measures the importance of attributes objectively and comprehensively this paper begins with putting forward the definition of approximate boundary accuracy based on algebra view. Afterwards, this paper proposes two concepts of relative information entropy and enhanced information entropy based on the definition of relative fuzzy entropy, which has obvious magnification effect. Then, two new methods of attribute reduction are proposed by incorporating approximate boundary precision into relative information entropy and enhanced information entropy, so that the choice of the importance of the attribute is more objective and comprehensive. Finally, it will analyze and compare the classification accuracy of each kind of algorithm by using the SVM classifier and ten-fold crossover method, and analyze the influence of outliers on the effect of the algorithm. Through experimental analysis and comparison, it can be concluded that the attribute reduction based on improved entropy is feasible and effective.