Supervised Pattern Recognition Involving Skewed Feature Densities

TL;DR

This paper compares Euclidean distance and a coincidence similarity index for supervised pattern recognition, demonstrating the dissimilarity index's superior performance with skewed feature densities and analyzing factors affecting classification accuracy.

Contribution

It introduces a comparison of two dissimilarity measures in supervised classification, highlighting the effectiveness of the coincidence similarity index for skewed data densities.

Findings

Dissimilarity index outperforms Euclidean distance with skewed densities.

Classification accuracy can be independent of the sharpness of data comparison.

Enhanced classification potential observed for datasets with right skewed feature densities.

Abstract

Pattern recognition constitutes a particularly important task underlying a great deal of scientific and technologica activities. At the same time, pattern recognition involves several challenges, including the choice of features to represent the data elements, as well as possible respective transformations. In the present work, the classification potential of the Euclidean distance and a dissimilarity index based on the coincidence similarity index are compared by using the k-neighbors supervised classification method respectively to features resulting from several types of transformations of one- and two-dimensional symmetric densities. Given two groups characterized by respective densities without or with overlap, different types of respective transformations are obtained and employed to quantitatively evaluate the performance of k-neighbors methodologies based on the Euclidean distance an coincidence similarity index. More specifically, the accuracy of classifying the intersection point between the densities of two adjacent groups is taken into account for the comparison. Several interesting results are described and discussed, including the enhanced potential of the dissimilarity index for classifying datasets with right skewed feature densities, as well as the identification that the sharpness of the comparison between data elements can be independent of the respective supervised classification performance.