基于随机森林的加权KNN算法

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI:10.1145/3318299.3318313

Huanian Zhang, Fanliang Bu

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引用次数: 3

摘要

本文提出了一种基于随机森林的加权KNN算法。该算法充分度量了各特征重要度的差异，克服了k近邻(KNN)算法对不平衡数据集和不同特征重要度数据集进行分类的缺点。有效提高了KNN算法的分类精度，并通过实验验证了所提算法的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Weighted KNN Algorithm Based on Random Forests

In this paper, we proposed a weighted KNN algorithm based on random forests. The proposed algorithm fully measures the differences in the importance of each feature, and overcomes the shortcoming of k-nearest neighbor (KNN) algorithm in classifying unbalanced data sets and data sets of different feature importance. The classification accuracy of the KNN algorithm is effectively improved, and the performance of the proposed algorithm is verified through experiments.

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International Conference on Machine Learning and Computing

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