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Data Editing for Neuro-Fuzzy Classifiers.

Gabrys, B., 2001. Data Editing for Neuro-Fuzzy Classifiers. In: Fourth International ICSC Symposium: Proceedings of the SOCO/ISFI’2001 Conference, June 26 - 29, 2001, Paisley, Scotland, 77.

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Gabrys_SOCO2001.pdf - Accepted Version



In this paper we investigate the potential benefits and limitations of various data editing procedures when constructing neuro-fuzzy classifiers based on hyperbox fuzzy sets. There are two major aspects of data editing which we are attempting to exploit: a) removal of outliers and noisy data; and b) reduction of training data size. We show that successful training data editing can result in constructing simpler classifiers (i.e. a classifier with a smaller number and larger hyperboxes) with better generalisation performance. However we also indicate the potential dangers of overediting which can lead to dropping the whole regions of a class and constructing too simple classifiers not able to capture the class boundaries with high enough accuracy. A more flexible approach than the existing data editing techniques based on estimating probabilities used to decide whether a point should be removed from the training set has been proposed. An analysis and graphical interpretations are given for the synthetic, non-trivial, 2-dimensional classification problems.

Item Type:Conference or Workshop Item (Paper)
Group:Faculty of Science & Technology
ID Code:8541
Deposited On:19 Dec 2008 20:01
Last Modified:14 Mar 2022 13:19


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