A Domain Knowledge as A Tool For Improving Classifiers
Bazan, Jan. G.
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.
rough set , concept approximation , ontology of concepts , discretization , behavioral pattern identification , automated planning