Lee, Kyungmi, and Estivill-Castro, Vladimir (2005) A hybrid classification approach to ultrasonic shaft signals. In: Proceedings of 17th Australian Joint Conference on Artificial Intelligence (3339) pp. 284-295. From: 17th Australian Joint Conference on Artificial Intelligence, 4 - 6 December 2004, Cairns, QLD, Australia.

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Abstract

In many applications of machine learning a series of feature extraction approaches and a series of classifiers are explored in order to obtain a system with the highest accuracy possible. In the application we discussed here at least two feature extraction approaches have been explored (Fast Fourier Transform) and Discrete Wavelet Transform, and at least two approaches to build classifiers have also been explored (Artificial Neural Networks and Support Vector Machines). If one combination seems superior in terms of accuracy rate, shall we adopt it as the one to use or is there a combination of the approaches that results in some benefit? We show here how we have combined classifiers considering the misclassification cost to obtain a more informative classification for its application in the field.

Item ID:	10136
Item Type:	Conference Item (Research - E1)
ISBN:	978-3-540-24059-4
ISSN:	1611-3349
Keywords:	industrial applications of AI; machine learning; multimodel systems; pattern analysis in signals; feature extraction; ensemble of classifiers
Date Deposited:	08 Jul 2010 05:50
FoR Codes:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 60% 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing @ 20% 08 INFORMATION AND COMPUTING SCIENCES > 0806 Information Systems > 080608 Information Systems Development Methodologies @ 20%
SEO Codes:	89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890205 Information Processing Services (incl. Data Entry and Capture) @ 80% 89 INFORMATION AND COMMUNICATION SERVICES > 8903 Information Services > 890399 Information Services not elsewhere classified @ 20%
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