Fuzzy partitions in learning from examples

Wiesław Traczyk

Abstract

Learning from examples is a popular methodology giving the set of rules (or decision trees) able to properly classify objects from predefined set. One of the main problems with this methodology is discretization — the process of converting continuous values of used attributes into more practical discrete values. Fuzzy partitions, introduced in this paper, can be viewed as a convenient way for expressing uncertainty in both: membership to discrete value and classification of cases, absent in the initial training set.
Author Wiesław Traczyk (FEIT / AK)
Wiesław Traczyk,,
- The Institute of Control and Computation Engineering
Pages536-542
Book Reusch Bernd (eds.): Computational Intelligence Theory and Applications, Lecture Notes In Computer Science, no. 1226, 1997, Springer Berlin Heidelberg, ISBN 978-3-540-62868-2, 978-3-540-69031-3
Keywords in EnglishArtificial Intelligence (incl. Robotics), Computation by Abstract Devices, Image Processing and Computer Vision, Mathematical Logic and Formal Languages, Systems and Information Theory in Engineering
URL http://link.springer.com/chapter/10.1007/3-540-62868-1_147
Score (nominal)3
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