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.
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Zhang, H., Hou, K., Zhou, Z. (2018). A Weighted KNN Algorithm Based on Entropy Method. In: Li, K., Fei, M., Du, D., Yang, Z., Yang, D. (eds) Intelligent Computing and Internet of Things. ICSEE IMIOT 2018 2018. Communications in Computer and Information Science, vol 924. Springer, Singapore. https://doi.org/10.1007/978-981-13-2384-3_41
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DOI: https://doi.org/10.1007/978-981-13-2384-3_41
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