Rough Set–Based Classification Systems

Robert K. Nowicki (Autor)

Buch | Hardcover

XIII, 188 Seiten

2019
Springer International Publishing (Verlag)
9783030038946 (ISBN)

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This book demonstrates an original concept for implementing the rough set theory in the construction of decision-making systems. It addresses three types of decisions, including those in which the information or input data is insufficient. Though decision-making and classification in cases with missing or inaccurate data is a common task, classical decision-making systems are not naturally adapted to it. One solution is to apply the rough set theory proposed by Prof. Pawlak.
The proposed classifiers are applied and tested in two configurations: The first is an iterative mode in which a single classification system requests completion of the input data until an unequivocal decision (classification) is obtained. It allows us to start classification processes using very limited input data and supplementing it only as needed, which limits the cost of obtaining data. The second configuration is an ensemble mode in which several rough set-based classification systems achieve the unequivocal decision collectively, even though the systems cannot separately deliver such results.

Introduction.- Rough Set Theory Fundamentals.- Rough Fuzzy Classification Systems.- Fuzzy Rough Classification Systems.- Rough Neural Network Classifier.- Rough Nearest Neighbour Classifier.- Ensembles of Rough Set-Based Classifiers.- Final Remarks.

Erscheinungsdatum	07.02.2019
Reihe/Serie	Studies in Computational Intelligence
Zusatzinfo	XIII, 188 p. 125 illus.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	473 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Themenwelt	Technik
Schlagworte	Classifiiers • Computational Intelligence • Decision Making • Fuzzy Rough Classifiers • Rough Neural Networks • Rough Sets Theory
ISBN-13	9783030038946 / 9783030038946
Zustand	Neuware