Abstract
This paper provides a new method to grow exploratory classification trees in multi-class problems. A two-stage algorithm, using recursively the latent budget model, is proposed to find ever finer partitions of objects into prior fixed number of groups. A new rule to assign the class labels to the children nodes is considered to deal with fuzzy data. Then, a software prototype namely E.T. Exploratory Trees, developed in the Matlab environment, is proposed to show the main features of the methodology through several interactive graphic tools1.
The present paper is financially supported by MIUR Funds 2001 awarded to R. Siciliano (Prot. N. 2001134928).
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Aria, M. (2005). Multi-Class Budget Exploratory Trees. In: Bock, HH., et al. New Developments in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27373-5_1
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DOI: https://doi.org/10.1007/3-540-27373-5_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23809-6
Online ISBN: 978-3-540-27373-8
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