Partial covers, reducts and decision rules in rough sets : theory and applications
This monograph is devoted to theoretical and experimental study of partial reducts and partial decision rules on the basis of the study of partial covers. The use of partial (approximate) reducts and decision rules instead of exact ones allows us to obtain more compact description of knowledge contained in decision tables, and to design more precise classifiers. Algorithms for construction of partial reducts and partial decision rules, bounds on minimal complexity of partial reducts and decision rules, and algorithms for construction of the set of all partial reducts and the set of all irreducible partial decision rules are considered. The book includes a discussion on the results of numerous experiments with randomly generated and real-life decision tables. These results show that partial reducts and decision rules can be used in data mining and knowledge discovery both for knowledge representation and for prediction. The results obtained in the monograph can be useful for researchers in such areas as machine learning, data mining and knowledge discovery, especially for those who are working in rough set theory, test theory and LAD (Logical Analysis of Data). The monograph can be used under the creation of courses for graduate students and for Ph. D. studies
Springer eBooks
1 online resource (xi, 150 pages) : illustrations
9783540690290, 9783540690276, 3540690298, 3540690271
288467904
Partial Covers, Reducts and Decision Rules
Partial Covers, Reducts and Decision Rules with Weights
Construction of All Irreducible Partial Covers, All Partial Reducts and All Irreducible Partial Decision Rules
Experiments with Real-Life Decision Tables
Universal Attribute Reduction Problem
English