Attributes regrouping by association rules in SUCRAGE

We are interested in a supervised learning method by automatic generation of classification rules: SUCRAGE. Premises construction is done by grouping the dependant attributes. This selection in one block of the features is realized by linear correlation research among the training set elements. Only...

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Bibliographic Details
Main Authors: Zaatour, Riadh, Borgi, Amel, Ben Slima, Ilef
Format: Conference Proceeding
Language:English
Subjects:
association rules
attributes regrouping
classification rules
Correlation
Engines
frequent itemsets
Itemsets
linear correlation
Partitioning algorithms
supervised classification
Supervised learning
Training
Uncertainty
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Summary:We are interested in a supervised learning method by automatic generation of classification rules: SUCRAGE. Premises construction is done by grouping the dependant attributes. This selection in one block of the features is realized by linear correlation research among the training set elements. Only numerical features can be taken into account by linear correlation search. In this article, we propose to extend SUCRAGE to handle symbolic attributes by using another method of regrouping attributes based on Association Rules. This method can detect different types of association between quantitative as well as qualitative attributes. The obtained results in generalization with various data using the built rules are very satisfactory.
ISSN:2378-2536
DOI:10.1109/SITA.2016.7772256