A Bonferroni Mean Based Fuzzy K-Nearest Centroid Neighbor Classifier

A Bonferroni Mean Based Fuzzy K-Nearest Centroid Neighbor Classifier

K-Nearest Neighbor, Nearest Centroid Neighborhood, Fuzzy K-Nearest Neighbor, local mean vector, Bonferroni mean

K-nearest neighbor (KNN) is an effective nonparametric classifier that determines the neighbors of a point based only on distance proximity. The classification performance of KNN is disadvantaged by the presence of outliers in small sample size datasets and its performance deteriorates on datasets with class imbalance. We propose a local Bonferroni Mean based Fuzzy K-Nearest Centroid Neighbor (BM-FKNCN) classifier that determines the class label of an unclassified sample dependent on the nearest local mean vector obtained using the Nearest Centroid Neighborhood (NCN) concept to better represent the underlying statistic of the dataset. The proposed classifier is robust towards outliers because the NCN concept also considers spatial distribution and geometrical placement of the neighbors. Also, the proposed classifier can overcome class domination of its neighbors in datasets with class imbalance because it averages all the centroid vectors from each class to adequately interpret the distribution of the classes. The BM-FKNCN classifier is tested on datasets that are taken from the Knowledge Extraction based on Evolutionary Learning (KEEL) repository and benchmarked with classification results from the KNN, Fuzzy-KNN (FKNN), BM-FKNN and FKNCN classifiers
The experimental results show that the BM-FKNCN achieves the highest overall average
classification accuracy of 89.86% compared to the other four classifiers.

Arya Widyadhana, Cornelius Bagus Purnama Putra, Rarasmaya Indraswari, Agus Zainal Arifin

http://dx.doi.org/10.21609/jiki.v14i1.959

Faculty of Computer Science Universitas Indonesia

2021-02-28

Sri Wahyuni

e-ISSN : 2502-9274 printed ISSN : 2088-7051

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Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)