Abstract
For good classiffication preprocessing is a key step. Good preprocessing reduces the noise in the data and retains most information needed for classiffication. Poor preprocessing on the other hand can make classiffication almost impossible. In this paper we evaluate several feature extraction methods in a special type of outlier detection problem, machine fault detection. We will consider measurements on water pumps under both normal and abnormal conditions. We use a novel data description method, called the Support Vector Data Description, to get an indication of the complexity of the normal class in this data set and how well it is expected to be distinguishable from the abnormal data.
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Tax, D.M.J., Ypma, A., Duin, R.P.W. (1999). Pump Failure Detection Using Support Vector Data Descriptions. In: Hand, D.J., Kok, J.N., Berthold, M.R. (eds) Advances in Intelligent Data Analysis. IDA 1999. Lecture Notes in Computer Science, vol 1642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48412-4_35
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DOI: https://doi.org/10.1007/3-540-48412-4_35
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