Most of Computational Intelligence models (e.g. neural networks or distance based methods) are designed to operate on continuous data and provide no tools to adapt their parameters to data described by symbolic values. Two new conversion methods which replace symbolic by continuous attributes are presented and compared to two commonly known ones. The advantages of the continuousification are illustrated with the results obtained with a neural network, SVM and a kNN systems for the converted data. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Gra̧bczewski, K., & Jankowski, N. (2003). Transformations of symbolic data for continuous data oriented models. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2714, 359–366. https://doi.org/10.1007/3-540-44989-2_43
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