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A Weighted KNN Algorithm Based on Entropy Method

  • Hui ZhangEmail author
  • Kaihu Hou
  • Zhou Zhou
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 924)

Abstract

Aiming at the problem that the classification accuracy of K-nearest neighbor algorithm is not high, this paper proposes a K-nearest neighbor algorithm that uses the weighted entropy method of Extreme value (EEM-KNN algorithm). The entropy method assigns weight to the sample’s feature index, and then introduces the weight of the feature index when calculating the distance between the query sample vector and the training sample vector. The four groups of classification data sets are used as test samples to test the effectiveness of the improved KNN algorithm, it also compares the difference between the improved algorithm and the traditional algorithm under different K values. Algorithms are implemented and tested on the Jupyter Notebook interactive platform. The improved KNN algorithm is verified by experiments, and the classification accuracy is improved.

Keywords

KNN algorithm Distance metric Entropy method Weighting 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Kunming University of Science and TechnologyKunmingChina

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