Inhibitory Rules in Data Analysis: A Rough Set ApproachSpringer Science & Business Media, 01/10/2008 - 116 من الصفحات This monograph is devoted to theoretical and experimental study of inhibitory decision and association rules. Inhibitory rules contain on the right-hand side a relation of the kind “attribut = value”. The use of inhibitory rules instead of deterministic (standard) ones allows us to describe more completely infor- tion encoded in decision or information systems and to design classi?ers of high quality. The mostimportantfeatureofthis monographis thatit includesanadvanced mathematical analysis of problems on inhibitory rules. We consider algorithms for construction of inhibitory rules, bounds on minimal complexity of inhibitory rules, and algorithms for construction of the set of all minimal inhibitory rules. We also discuss results of experiments with standard and lazy classi?ers based on inhibitory rules. These results show that inhibitory decision and association rules can be used in data mining and knowledge discovery both for knowledge representation and for prediction. Inhibitory rules can be also used under the analysis and design of concurrent systems. 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 logical analysis of data (LAD). The monograph can be used under the creation of courses for graduate students and for Ph.D. studies. TheauthorsofthisbookextendanexpressionofgratitudetoProfessorJanusz Kacprzyk, to Dr. Thomas Ditzinger and to the Studies in Computational Int- ligence sta? at Springer for their support in making this book possible. |
المحتوى
Introduction | 1 |
Maximal Consistent Extensions of Information Systems | 9 |
Minimal Inhibitory Association Rules for Almost All kValued Information Systems | 30 |
Partial Covers and Inhibitory Decision Rules | 43 |
Partial Covers and Inhibitory Decision Rules with Weights | 63 |
Classifiers Based on Deterministic and Inhibitory Decision Rules | 80 |
Lazy Classification Algorithms Based onDeterministic and Inhibitory Association Rules | 87 |
Lazy Classification Algorithms Based on Deterministic and Inhibitory Decision Rules | 99 |
Final Remarks | 107 |
References | 109 |
115 | |
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عبارات ومصطلحات مألوفة
10-fold cross-validation a-cover attributes a1 b₁ based on deterministic based on inhibitory cardinality characteristic table classifiers based Cmin(a conditional attributes considered algorithm D-classifier DD-algorithm Dec(T decision attached decision attribute decision rule problem decision system decision table deterministic decision rules deterministic rules Eq(T error rate evaluation function exact cover exists a rule Ext¹(S Ext³ greedy algorithm Heidelberg ID-algorithm inconsistent equation systems inhibitory association rules inhibitory decision rules j-th k-valued information systems labeled with attributes lazy learning Lemma Let us consider Lmin log₂ logk lower bound lymphography machine learning maximal consistent extensions minimal inhibitory rules Moshkov node nonterminal nodes obtained partial covers partial inhibitory decision Piliszczuk polynomial algorithm polynomial approximate algorithms problem with weights Proposition R¹(T real number results of experiments right-hand side rough set Sect set cover problem Skowron subset system of equations Theorem true and realizable tuple upper bound weight function Zielosko 47