Introduction to mathematical logic; (3rd ed.)
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Machine learning, neural and statistical classification
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Neighborhood based detection of anomalies in high dimensional spatio-temporal sensor datasets
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Transactions on Rough Sets IX
Case-based Planning of Treatment of Infants with Respiratory Failure
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Wisdom Technology: A Rough-Granular Approach
Aspects of Natural Language Processing
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HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Classification trees for time series
Pattern Recognition
Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
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Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
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This paper investigates the approaches to an improvement of classifiers quality through the application of a domain knowledge. The expertise may be utilizable on several levels of decision algorithms such as: feature extraction, feature selection, a definition of temporal patterns used in an approximation of the concepts, especially of the complex spatio-temporal ones, an assignment of an object to the concept and a measurement of the objects similarity. The domain knowledge incorporation results then in the reduction of the size of searched spaces. The work constitutes an overview of classifier building methods efficiently utilizing the expertise, worked out latterly by Professor Andrzej Skowron research group. The methods using domain knowledge intended to enhance the quality of classic classifiers, to identify the behavioral patterns and for automatic planning are discussed. Finally it answers a question whether the methods satisfy the hopes vested in them and indicates the directions for future development.