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Data mining with decision trees to extract features of sandalwood odorous molecules

Mohamed Kissi, Mohammed Ramdani


In this work, a new approach to the recognition of odors, based on decision trees, is presented. This approach has been implemented using a database of sandalwood molecules and an expert knowledge for these molecules. Decision trees are used to learn from this database a powerful rule set determining the presence or the absence of odor. For a better prediction, the method uses an aggregation between molecule descriptors. We apply three aggregation operators: Zadeh, Lukasiewicz, and Ordered Weighted Averaging. The main olfactory descriptor was correctly predicted by this method for 88 percent of the elements of the testing set.


Data mining, Decision trees, Aggregation operators, Structure-Odor Relationships, Modelling.

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