Classification ensembles for shaft test data: empirical evaluation
Lee, Kyungmi, and Estivill-Castro, Vladimir (2005) Classification ensembles for shaft test data: empirical evaluation. International Journal of Simulation, 6 (10-11). pp. 38-47.
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A-scans from ultrasonic testing of long shafts are complex signals. The discrimination of different types of echoes is of importance for nondestructive testing and equipment maintenance. Research has focused on selecting features of physical significance or exploring classifier like artificial neural networks and support vector machines. This paper confirms the observation that there seems to be uncorrelated errors among the variants explored in the past, and therefore an ensemble of classifiers is to achieve better discrimination accuracy. We explore the diverse possibilities of heterogeneous and homogeneous ensembles, combination techniques, feature extraction methods and classifiers types and determine guidelines for heterogeneous combinations that result in superior performance.
|Item Type:||Article (Refereed Research - C1)|
This paper is identical as follows:
Lee, Kyungmi (Joanne), and Estivill-Castro, Vladimir (2004) Classification Ensembles for Shaft Test Data: Empirical Evaluation. ISBN 0-7695-2291-2 . Proceedings of the Fourth International Conference on Hybrid Intelligent Systems In: 4th International Conference on Hybrid Intelligent Systems (HIS04), 5-8 December 2004, Kitakyushu, Japan.
|Keywords:||pattern analysis in signals; feature extraction; ensemble of classifiers; artificial neural networks; support vector machines|
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08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 60%
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|Deposited On:||08 Apr 2010 12:38|
|Last Modified:||12 Feb 2011 22:58|
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