Hybrid distance measurement method and application based on conditional probability distribution

doi:10.3969/j.issn.1000-1565.2025.05.008

Abstract

Abstract: To improve the recognition accuracy of nominal attribute instance differences and optimize the performance of classification algorithms, a hybrid distance measurement method based on conditional probability distribution is proposed, which comprehensively considers the attributes and category features of instances.Firstly, this method calculates the differences in conditional probability distributions among attributes and between attributes and categories. Secondly, a new hybrid distance metric is obtained by using mutual information to weight and combine the two differences. Finally, simulation experiments are performed on 20 UCI datasets based on K-nearest neighbor algorithm. Meanwhile, the distance measurement method is applied to the diagnosis and treatment of appendicitis in children. The results show that compared with the three measurement methods including overlap measurement, the distance measurement method proposed in this work significantly improves the accuracy of the classification algorithm and has good application prospects.

Key words: conditional probability distribution, hybrid distance metric, mutual information, nominal attribute

CLC Number:

TP301.6

HU Guikai, YANG Peirong. Hybrid distance measurement method and application based on conditional probability distribution[J]. Journal of Hebei University(Natural Science Edition), 2025, 45(5): 520-529.

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