Transactions on Rough Sets IV / edited by James F. Peters, Andrzej Skowron.

Springer eBooks

Regular Papers -- A Treatise on Rough Sets -- On Optimization of Decision Trees -- Dealing with Missing Data: Algorithms Based on Fuzzy Set and Rough Set Theories -- Characteristic Relations for Incomplete Data: A Generalization of the Indiscernibility Relation -- Supervised Learning in the Gene Ontology Part I: A Rough Set Framework -- Supervised Learning in the Gene Ontology Part II: A Bottom-Up Algorithm -- Comparative Analysis of Deterministic and Nondeterministic Decision Tree Complexity Local Approach -- A Fast Host-Based Intrusion Detection System Using Rough Set Theory -- Incremental Learning and Evaluation of Structures of Rough Decision Tables -- Dissertations and Monographs -- A Framework for Reasoning with Rough Sets -- Analogy-Based Reasoning in Classifier Construction.

The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets and theory of evidence. This fourth volume of the Transactions on Rough Sets opens with an introductory article by Zdzislaw Pawlak, the originator of rough sets. Seven papers explore the theory of rough sets in various domains: a framework for reasoning with rough sets utilizing extended logic programs, optimization of decision trees, fuzzy set and rough set approaches to dealing with missing data, generalization of the indiscernibility relation as an aid to dealing with incompletely specified decision tables, deterministic and non-deterministic decision tree complexity in the context of both finite and infinite information systems, analogy-based reasoning in classifier construction, and incremental learning and evaluation of structures of rough decision tables. In addition, two papers in this volume introduce new applications of rough sets, namely, supervised learning in the gene ontology and the design of an intrusion detection system.

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