Fuzzy learning vector quantization approaches for interval data

Symbolic data analysis deals with complex data types, capable of modeling internal data variability and imprecise data. This paper introduces two Fuzzy Learning Vector Quantization algorithms for interval symbolic data. One algorithm employs an interval Euclidean distance. The second uses a weighted interval Euclidean distance to try and achieve a better performance of classification when the data set is composed of classes with varying sizes, shapes and structures. The algorithms are evaluated for their performances with synthetic and real data sets. This paper aims at contributing to the area of Supervised Learning within Symbolic Data Analysis.