Fuzzy partitions in learning from examples
AbstractLearning 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.
|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 English||Artificial Intelligence (incl. Robotics), Computation by Abstract Devices, Image Processing and Computer Vision, Mathematical Logic and Formal Languages, Systems and Information Theory in Engineering|
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