Extending learning vector quantization for classifying data with categorical values

Ning Chen, Nuno C. Marques

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

Learning vector quantization (LVQ) is a supervised neural network method applicable in non-linear separation problems and widely used for data classification. Existing LVQ algorithms are mostly focused on numerical data. This paper presents a batch type LVQ algorithm used for classifying data with categorical values. The batch learning rules make possible to construct the learning methodology for data in categorical nonvector spaces. Experiments on UCI data sets demonstrate the proposed algorithm is effective to improve the capability of standard LVQ to handle data with categorical values.

Original languageEnglish
Title of host publicationAgents and Artificial Intelligence - International Conference, ICAART 2009, Revised Selected Papers
Pages124-136
Number of pages13
DOIs
Publication statusPublished - 2010
Event1st International Conference on Agents and Artificial Intelligence, ICAART 2009 - Porto, Portugal
Duration: 19 Jan 200921 Jan 2009

Publication series

NameCommunications in Computer and Information Science
Volume67 CCIS
ISSN (Print)1865-0929

Conference

Conference1st International Conference on Agents and Artificial Intelligence, ICAART 2009
CountryPortugal
CityPorto
Period19/01/0921/01/09

Keywords

  • Batch learning
  • Categorical value
  • Learning vector quantization
  • Self-organizing map

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