Incremental Attribute Reduction Method Based on Chi-Square Statistics and Information Entropy

Abstract

Attributes in datasets are usually not equally significant. Some attributes are unnecessary or redundant. Attribute reduction is an important research issue of rough set theory, which can find minimum subsets of attributes with the same classification effect as the whole dataset by removing unnecessary or redundant attributes. We use Chi-square statistics to evaluate the significance of condition attributes. It can reduce the search space of attribute reduction and improve the speed of attribute reduction. Conditional entropy of relative attributes is adopted as a heuristic function. Two decision table reduction algorithms, forward selection and backward deletion, are proposed to approach the optimal solution. Based on this, an efficient incremental attribute reduction method for dynamically changing datasets is proposed by preserving intermediate variables. The intermediate variable is the observation frequency matrix of joint events of each condition attribute and decision attribute. Experimental results show that the proposed algorithms can improve performance in terms of processing time.

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“…Figure 9 illustrates the estimation results of KDE. To examine the goodness-of-fit, the chi-square test is performed with the significant level set at 0.05 and the degree of freedom at 49 (Su et al, 2020). The test results of the EV and PV are 23.53 and 42.40 respectively, which are both below the critical value 66.34 and validate the accuracy of KDE.…”