CRITICAL BOUNDARY VECTOR CONCEPT IN NEAREST NEIGHBOR CLASSIFIERS USING K-MEANS CENTERS FOR EFFICIENT TEMPLATE REDUCTION

Wenjun Xia, Tadashi Shibata

2011

Abstract

Dealing with large data sets, the computational cost and resource demands using the nearest neighbor (NN) classifier can be prohibitive. Aiming at efficient template condensation, this paper proposes a template re-duction algorithm for NN classifier by introducing the concept of critical boundary vectors in conjunction with K-means centers. Initially K-means centers are used as substitution for the entire template set. Then, in order to enhance the classification performance, critical boundary vectors are selected according to a newly proposed training algorithm which completes with only single iteration. COIL-20 and COIL-100 databases were utilized for evaluating the performance of image categorization in which the bio-inspired directional-edge-based image feature representation (Suzuki and Shibata. 2004) was employed. UCI iris and UCI Landsat databases were also utilized to evaluate the system for other classification tasks using numerical-valued vectors. Experimental results show that by using the reduced template sets, the proposed algorithm shows a superior performance to NN classifier using all samples, and comparable to Support Vector Machines using Gaussian kernel which are computationally more expensive.

Paper Citation

in Harvard Style

Xia W. and Shibata T. (2011). CRITICAL BOUNDARY VECTOR CONCEPT IN NEAREST NEIGHBOR CLASSIFIERS USING K-MEANS CENTERS FOR EFFICIENT TEMPLATE REDUCTION . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 93-98. DOI: 10.5220/0003642600930098

in Bibtex Style

@conference{ncta11,
author={Wenjun Xia and Tadashi Shibata},
title={CRITICAL BOUNDARY VECTOR CONCEPT IN NEAREST NEIGHBOR CLASSIFIERS USING K-MEANS CENTERS FOR EFFICIENT TEMPLATE REDUCTION},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={93-98},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003642600930098},
isbn={978-989-8425-84-3},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - CRITICAL BOUNDARY VECTOR CONCEPT IN NEAREST NEIGHBOR CLASSIFIERS USING K-MEANS CENTERS FOR EFFICIENT TEMPLATE REDUCTION
SN - 978-989-8425-84-3
AU - Xia W.
AU - Shibata T.
PY - 2011
SP - 93
EP - 98
DO - 10.5220/0003642600930098