Rough Set-Based Classification Systems (Hardcover, 1st ed. 2019)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 802
Release date:	2019
First published:	2019
Authors:	Robert K. Nowicki
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	188
Edition:	1st ed. 2019
ISBN-13:	978-3-03-003894-6
Categories:	Books > Reference & Interdisciplinary > Communication studies > Decision theory > General Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-03-003894-7
Barcode:	9783030038946

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