A weighted learning vector quantization approach for interval data

Authors:
Telmo M. Silva Filho;Renata Maria Cardoso R. de Souza
Affiliations:
Centro de Informatica, Universidade Federal de Pernambuco, Recife, PE, Brazil;Centro de Informatica, Universidade Federal de Pernambuco, Recife, PE, Brazil
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Year:
2012

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Abstract

Symbolic Data Analysis deals with complex data types, capable of modeling internal data variability and imprecise data. This paper introduces a Learning Vector Quantization algorithm for symbolic data that uses a weighted interval Euclidean distance to try and achieve a better performance of classification when the dataset is composed of classes of varying structures. This algorithm is compared to a Learning Vector Quantization algorithm that uses traditional interval Euclidean distance. The algorithms are evaluated and compared for their performances with synthetic and real datasets. This paper aims at contributing to the area of Supervised Learning within Symbolic Data Analysis.