DOI: https://doi.org/10.14710/medstat.17.2.162-172

PARAMETER INDEPENDENT FUZZY WEIGHTED k-NEAREST NEIGHBOR

*Mayawi Mayawi  -  Department of Mathematics, Universitas Gadjah Mada, Bulaksumur, Caturtunggal, Kec. Depok, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia
Subanar Subanar  -  Department of Mathematics, Universitas Gadjah Mada, Bulaksumur, Caturtunggal, Kec. Depok, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia
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Abstract
Parameter Independent Fuzzy Weighted k-Nearest Neighbor (PIFWkNN) as a classification technique developed by combining Success History based Parameter Adaptive Differential Evolution (SHADE) with Fuzzy k-Nearest Neighbor (FkNN), where this PIFWkNN does not state the optimization of weights and k values as two separate problems, but they’re combined into one and solved simultaneously by the SHADE algorithm. The steps for implementing the PIFWkNN method are explained, followed by its application to 10 different datasets, and then the accuracy is calculated. To see the consistency of the goodness of the classification of this method, the accuracy results are compared with the accuracy of the k-Nearest Neighbor (kNN), FkNN, and Weighted k-Nearest Neighbor (WkNN). The results show that the average accuracy of PIFWkNN, kNN, FkNN, and WkNN is 75.76%, 68.52%, 71.40% and 66.22% so PIFWkNN is higher than the three methods. Using the Wilcoxon Sign Rank (WSR) test also concluded that with a 95% confidence shows that every hypothesis had significant differences. Furthermore, it descriptively shows that the average rank of PIFWkNN is higher than the other. Thus, the PIFWkNN has higher accuracy than the kNN, FkNN, and WkNN.
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Keywords: Classification; Parameter Independent Fuzzy Weighted k-Nearest Neighbor; k-Nearest Neighbor; Weighted k-Nearest Neighbor; Fuzzy k-Nearest Neighbor; Success History based Parameter Adaptive Differential Evolution

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Language : EN
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