Attributes regrouping by association rules in SUCRAGE
| Autor: | Riadh Zaatour, Amel Borgi, Ilef Ben Slima |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Training set Association rule learning Generalization Computer science business.industry Association (object-oriented programming) Supervised learning 02 engineering and technology computer.software_genre Machine learning Correlation 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Selection (linguistics) 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer Block (data storage) |
| Zdroj: | SITA |
| DOI: | 10.1109/sita.2016.7772256 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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