Integration of prior knowledge of measurement noise in kernel density classification |
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Authors: | Yunlei Li Dick de Ridder Robert PW Duin Marcel JT Reinders |
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Affiliation: | 1. Children''s Allergy Service, St Thomas'' Hospital, London, United Kingdom;2. Medical Student, Kings College Medical School, London, United Kingdom;1. Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-C?ur de Montréal, Montreal, Quebec, Canada;2. Department of Psychology, Université de Montréal, Montreal, Quebec, Canada;3. Department of Psychiatry, Université de Montréal, Montreal, Quebec, Canada;4. Department of Neurology, Montreal General Hospital, Montreal, Quebec, Canada;5. Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada;1. Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark;2. Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, University of Copenhagen, Glostrup Hospital, Glostrup, Denmark;3. H. Lundbeck A/S, Copenhagen, Denmark;4. Department of Neurology, Bispebjerg Hospital, Copenhagen, Denmark |
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Abstract: | Samples can be measured with different precisions and reliabilities in different experiments, or even within the same experiment. These varying levels of measurement noise may deteriorate the performance of a pattern recognition system, if not treated with care. Here we seek to investigate the benefit of incorporating prior knowledge about measurement noise into system construction. We propose a kernel density classifier which integrates such prior knowledge. Instead of using an identical kernel for each sample, we transform the prior knowledge into a distinct kernel for each sample. The integration procedure is straightforward and easy to interpret. In addition, we show how to estimate the diverse measurement noise levels in a real world dataset. Compared to the basic methods, the new kernel density classifier can give a significantly better classification performance. As expected, this improvement is more obvious for small sample size datasets and large number of features. |
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