Attribute reduction algorithm based on relation coefficient and conditional information entropy

Attribute reduction in rough set theory is a NP-hard problem,which is studied mainly to design a more efficient algorithm.Aiming at the problem of inefficiency and low velocity with the traditional attribute reduction algorithm,an attribute reduction algorithm based on correlation coefficient and conditional information entropy is proposed,which changes attribute reduction process of non core attributes in the decision table into calculation of correlation coefficient,reduces the number of scanning decision table,algorithmic time complexity and redundancy of the algorithm,and improves the efficiency of attribute reduction.Then the k-fold rotation comparison method is used to calculate correlation coefficient,which largely reduces calculation amount,and attains sub optimal attribute reduction result.The algorithm details are given,and an experiment is carried out,the result of which verifies the efficiency of the algorithm.