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| Titel |
Gas chimney detection based on improving the performance of combined multilayer perceptron and support vector classifier |
| VerfasserIn |
H. Hashemi, D. M. J. Tax, R. P. W. Duin, A. Javaherian, P. Groot |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 15, no. 6 ; Nr. 15, no. 6 (2008-11-21), S.863-871 |
| Datensatznummer |
250012803
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| Publikation (Nr.) |
 copernicus.org/npg-15-863-2008.pdf |
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| Zusammenfassung |
| Seismic object detection is a relatively new field in
which 3-D bodies are visualized and spatial relationships between objects of
different origins are studied in order to extract geologic information. In
this paper, we propose a method for finding an optimal classifier with the
help of a statistical feature ranking technique and combining different
classifiers. The method, which has general applicability, is demonstrated
here on a gas chimney detection problem. First, we evaluate a set of input
seismic attributes extracted at locations labeled by a human expert using
regularized discriminant analysis (RDA). In order to find the RDA score for
each seismic attribute, forward and backward search strategies are used.
Subsequently, two non-linear classifiers: multilayer perceptron (MLP) and
support vector classifier (SVC) are run on the ranked seismic attributes.
Finally, to capitalize on the intrinsic differences between both
classifiers, the MLP and SVC results are combined using logical rules of
maximum, minimum and mean. The proposed method optimizes the ranked feature
space size and yields the lowest classification error in the final combined
result. We will show that the logical minimum reveals gas chimneys that
exhibit both the softness of MLP and the resolution of SVC classifiers. |
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