Modified fuzzy k-nearest centroid neighbor method with Chebyshev distance
Bibit Waluyo Aji, Aisyah Nur Adillah, Dewi Septiarti, Bambang Irawanto, Bayu Surarso, Farikhin, Yosza Dasril · AIP conference proceedings · 2024
Fuzzy K-Nearest Centroid Neighbor is the classification method used to predict test data that uses sample membership values that are not yet clear to all available classes.Chebyshev distance is described because of the best distinction among vectors alongside any coordinate dimension.In other words, it's the most distance alongside one axis.Due to its nature, it's frequently known as Chessboard distance because the minimal variety of actions wanted through a king to head from one rectangular to any other is the same as Chebyshev distance.This study aimed to determine the effect of Chebyshev distance on FKNCN performance.The research shows that using the Chebyshev distance produces higher accuracy than using the Euclidean distance, so it improves the performance of the FKNCN.