Abstract
In this study, we introduce a BDD — based pattern classifier. The essence of the proposed approach lies in a binarization of continuous data representoin of an original classification data in the form of a binary decision diagram (BDD). The resulting BDD helps compress the data, reveal the most essential binary features and complete classification. It is shown that such BDDs can serve as a digital blueprint of the underlying classifiers.
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Pedrycz, W., Sosnowski, Z.A. (2005). Genetic BDD-oriented Pattern Classifiers. In: Saeed, K., Pejaś, J. (eds) Information Processing and Security Systems. Springer, Boston, MA. https://doi.org/10.1007/0-387-26325-X_26
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DOI: https://doi.org/10.1007/0-387-26325-X_26
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-25091-5
Online ISBN: 978-0-387-26325-0
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