Fuzzy Hyperline Segment Neural Network Pattern Classifier with Different Distance Metrics

Abstract

The Fuzzy Hyperline Segment Neural Network (FHLSNN) pattern classifier utilizes fuzzy set as pattern classes in which each fuzzy set is a union of fuzzy set hyperline segments. The Euclidean distance metric is used to compute the distances to decide the degree of membership function. In this paper, the use of other various distance metrics such as Manhattan, Squared Euclidean, Canberra and Chebyshew distance metrics is proposed. The performance of FHLSNN pattern classifier is evaluated with various benchmark databases such as Glass, Wine, PID, and Iris data set and real handwritten database. The FHLSNN pattern classifier is evaluated for generalization performance under recognition rate, training time and testing time. From the result analysis, the performance of classifier is based on the distance metrics as well the database used is verified. This analysis will help to select a suitable distance metric for fuzzy neural network classifier for particular application.

Performance Evaluation of K-means Clustering Algorithm with Various Distance Metrics

“…To describe an aforementioned theory, we consider the example of Euclidean distance and the Manhattan distance [15] [16]. Euclidean distance is the direct straight line geometrical distance between the two points as shown in.…”