Combinatorial Machine Learning: A Rough Set Approach (Studies in Computational Intelligence, 360)

Decision trees and decision rule systems are widely used in different applications as algorithms for problem solving, as predictors, and as a way for knowledge representation. Reducts play key role in the problem of attribute (feature) selection. The aims of this book are (i) the consideration of the sets of decision trees, rules and reducts; (ii) study of relationships among these objects; (iii) design of algorithms for construction of trees, rules and reducts; and (iv) obtaining bounds on their complexity. Applications for supervised machine learning, discrete optimization, analysis of acyclic programs, fault diagnosis, and pattern recognition are considered also. This is a mixture of research monograph and lecture notes. It contains many unpublished results. However, proofs are carefully selected to be understandable for students. The results considered in this book can be useful for researchers in machine learning, data mining and knowledge discovery, especially for those who are working in rough set theory, test theory and logical analysis of data. The book can be used in the creation of courses for graduate students. Front Matter....Pages - Front Matter....Pages 1-3 Examples from Applications....Pages 5-20 Front Matter....Pages 21-21 Sets of Tests, Decision Rules and Trees....Pages 23-36 Bounds on Complexity of Tests, Decision Rules and Trees....Pages 37-46 Algorithms for Construction of Tests, Decision Rules and Trees....Pages 47-67 Decision Tables with Many-Valued Decisions....Pages 69-86 Approximate Tests, Decision Trees and Rules....Pages 87-109 Front Matter....Pages 111-111 Supervised Learning....Pages 113-126 Local and Global Approaches to Study of Trees and Rules....Pages 127-142 Decision Trees and Rules over Quasilinear Information Systems....Pages 143-153 Recognition of Words and Diagnosis of Faults....Pages 155-170 Back Matter....Pages -