MICSL: Multiple Iterative Constraint Satisfaction based Learning |
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| Affiliation: | 1. China University of Political Science and Law, Department of Sociology, 27 Fuxue Road, Beijing 102249, China;2. Peking University, School of Education, 5 Yiheyuan Road, Beijing 100871, China;3. Northwestern University, Department of Sociology, 1810 Chicago Avenue, Evanston, IL 60208, United States of America;1. Department of Health and Exercise Science, Neuromuscular Research Laboratory, University of Oklahoma, Norman, Oklahoma, USA;2. Department of Athletics, Basketball Strength and Performance, University of Oklahoma, Norman, Oklahoma, USA;3. Department of Health and Exercise Science, Sensory and Muscle Function Research Laboratory, University of Oklahoma, USA;4. Departmeny of Health and Exercise Science, Sport, Health, and Exercise Data Analytics Laboratory, University of Oklahoma, Norman, Oklahoma, USA;5. Departmeny of Health and Exercise Science, Bone Density Research Laboratory, University of Oklahoma, Norman, Oklahoma, USA |
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| Abstract: | 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. |
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