Open Access Open Access  Restricted Access Subscription or Fee Access

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

Mohamed Kissi, Mohammed Ramdani

Abstract


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.

Keywords


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

Full Text:

PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.