In sample classification, the fuzzy k-nearest neighbor (FkNN) method and the associate improved classification algorithms ignore the uneven distribution of samples and the noise samples, thus are unable to reflect the differences of class sample features, resulting in the low classification accuracy. In order to overcome the limitations, a fuzzy weighted k-nearest neighbor data classification method based on affinity is proposed in this paper. Firstly, the membership of samples is calculated based on affinity among samples. Then, the feature weights of class samples are determined by the fuzzy entropy values, and k-neighbors are selected according to the weighted Euclidean distance. Finally, the samples will be classified according to the fuzzy membership of the samples belong to each class. The experimental results on the UCI datasets show that the proposed method is effective.
LIU Cheng-cheng, JIANG Ying
. Data Classification Method of Fuzzy Weighted k-Nearest Neighbor Based on Affinity[J]. Journal of Applied Sciences, 2018
, 36(4)
: 679
-688
.
DOI: 10.3969/j.issn.0255-8297.2018.04.011
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