A Bonferroni Mean Based Fuzzy K Nearest Centroid Neighbor Classifier

  • Arya Widyadhana Institut Teknologi Sepuluh Nopember
  • Cornelius Bagus Purnama Putra Institut Teknologi Sepuluh Nopember
  • Rarasmaya Indraswari Institut Teknologi Sepuluh Nopember
  • Agus Zainal Arifin Institut Teknologi Sepuluh Nopember
Keywords: K-Nearest Neighbor (KNN), Nearest Centroid Neighborhood (NCN), Fuzzy K-Nearest Neighbor (FKNN), local mean vector, Bonferroni mean

Abstract

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 assigns class label of a query sample dependent on the nearest local centroid mean vector to better represent the underlying statistic of the dataset. The proposed classifier is robust towards outliers because the Nearest Centroid Neighborhood (NCN) concept also considers spatial distribution and symmetrical 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 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.

Author Biographies

Arya Widyadhana, Institut Teknologi Sepuluh Nopember
Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology
Cornelius Bagus Purnama Putra, Institut Teknologi Sepuluh Nopember
Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology
Rarasmaya Indraswari, Institut Teknologi Sepuluh Nopember
Department of Information Systems, Faculty of Intelligent Electrical and Informatics Technology
Agus Zainal Arifin, Institut Teknologi Sepuluh Nopember
Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology
Published
2021-02-28