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Data Editing for Neuro-Fuzzy Classifiers
| Content Provider | Semantic Scholar |
|---|---|
| Author | Gabrys, Bogdan |
| Copyright Year | 2001 |
| Abstract | 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://eprints.bournemouth.ac.uk/8541/1/Gabrys_SOCO2001.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
