Improving the K-NN classification with the euclidean distance through linear data transformations

Authors:
Leon Bobrowski;Magdalena Topczewska
Affiliations:
Faculty of Computer Science, Bialystok Technical University;Faculty of Computer Science, Bialystok Technical University
Venue:
ICDM'04 Proceedings of the 4th international conference on Advances in Data Mining: applications in Image Mining, Medicine and Biotechnology, Management and Environmental Control, and Telecommunications
Year:
2004

Citing 5
Cited 3

Applied multivariate statistical analysis

Applied multivariate statistical analysis
Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Case-based reasoning

Case-based reasoning
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Data mining on multimedia data

Data mining on multimedia data

Case-Based Reasoning and the Statistical Challenges

ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Using clustering to learn distance functions for supervised similarity assessment

Engineering Applications of Artificial Intelligence
Improving performance of self-organising maps with distance metric learning method

ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

One of the most popular techniques in pattern recognition applications is the nearest neighbours (K-NN) classification rule based on the Euclidean distance function. This rule can be modified by data transformations. Variety of distance functions can be induced from data sets in this way. We take into considerations inducing distance functions by linear data transformations. The results of our experiments show the possibility of improving K-NN rules through such transformations.