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| Titel: |
MICSL: Multiple Iterative Constraint Satisfaction based Learning |
| Auteur: |
Potamias, George |
| Verschenen in: |
Intelligent data analysis |
| Paginering: | Jaargang 3 (2013) nr. 4 pagina's 245-265 |
| Jaar: |
2013-06-14 |
| Inhoud: |
A novel concept learning algorithm named, MICSL: Multiple Iterative Constraint Satisfaction based Learning, is presented. The algorithm utilizes mathematical programming and constraint satisfaction techniques towards uniform representation and management of both data and background knowledge. It offers a flexible enough learning framework and respective services. The representation flexibility of MICSL rests on a method that transforms propositional cases, represented as propositional clauses, into constraint equivalents. The theoretical background as well as the validity of the transformation process are analyzed and studied. Following a ‘general-to-specific’ generalization strategy the algorithm iterates on multiple calls of a constraint satisfaction process. The outcome is a consistent set of rules. Each rule composes a minimal model of the given set of cases. Theoretical results relating the solutions of a constraint satisfaction process and the minimal models of a set of cases are stated and proved. The performance of the algorithm on some real-world benchmark domains is assessed and compared with widely used machine learning systems, such as C4.5 and CN2. Issues related to the algorithm's complexity are also raised and discussed. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 8 van 11 gevonden artikelen
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