An Accurate Method of Determining Attribute Weights in Distance-Based Classification Algorithms

Abstract

Weight determination aims to determine the importance of different attributes; determining accurate weights can significantly improve the accuracy of classification and clustering. This paper proposes an accurate method for attribute weight determination. The method uses the distance from the sample point of each class to the class center point. It can minimize the weights and determines the attribute weights of the constraints through the objective function. In this paper, the attribute weights obtained by the exact solution are applied to the K-means clustering algorithm; three classic machine learning data sets, the iris data set, the wine data set, and the wheat seed data set, are clustered. Using the normalized mutual information as the evaluation index, a confusion matrix was established. Finally, the clustering results are visualized and compared with other methods to verify the effectiveness of the proposed method. The results show that this method improves the normalized mutual information by 0.11 and 0.08, respectively, compared with the unweighted and entropy weighted methods for iris clustering results. Furthermore, the performance on the wine data set is improved by 0.1, and the performance on the wheat seed data set is improved by 0.15 and 0.05.