A BATCH LEARNING VECTOR QUANTIZATION ALGORITHM FOR CATEGORICAL DATA

Ning Chen, Nuno C. Marques

2009

Abstract

Learning vector quantization (LVQ) is a supervised learning algorithm for data classification. Since LVQ is based on prototype vectors, it is a neural network approach particularly applicable in non-linear separation problems. Existing LVQ algorithms are mostly focused on numerical data. This paper presents a batch type LVQ algorithm used for mixed numerical and categorical data. Experiments on various data sets demonstrate the proposed algorithm is effective to improve the capability of standard LVQ to deal with categorical data.

Paper Citation

in Harvard Style

Chen N. and C. Marques N. (2009). A BATCH LEARNING VECTOR QUANTIZATION ALGORITHM FOR CATEGORICAL DATA . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8111-66-1, pages 77-84. DOI: 10.5220/0001661700770084

in Bibtex Style

@conference{icaart09,
author={Ning Chen and Nuno C. Marques},
title={A BATCH LEARNING VECTOR QUANTIZATION ALGORITHM FOR CATEGORICAL DATA},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2009},
pages={77-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001661700770084},
isbn={978-989-8111-66-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - A BATCH LEARNING VECTOR QUANTIZATION ALGORITHM FOR CATEGORICAL DATA
SN - 978-989-8111-66-1
AU - Chen N.
AU - C. Marques N.
PY - 2009
SP - 77
EP - 84
DO - 10.5220/0001661700770084