Abstract
Based on the concept of patterns, fundamental for the Logical Analysis of Data (LAD), we define a numerical score associated to every observation in a dataset, and show that its use allows the classification of most of the observations left unclassified by LAD. The accuracy of this extended LAD classification is compared on several publicly available benchmark datasets to that of the original LAD classification, and to that of the classifications provided by the most frequently used statistical and data mining methods.
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Alexe, S., Hammer, P.L. (2007). Pattern-Based Discriminants in the Logical Analysis of Data. In: Pardalos, P.M., Boginski, V.L., Vazacopoulos, A. (eds) Data Mining in Biomedicine. Springer Optimization and Its Applications, vol 7. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-69319-4_1
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DOI: https://doi.org/10.1007/978-0-387-69319-4_1
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-69318-7
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