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Outlier detection can be treated as a part of the data preprocess or as the object of data mining. There is still no effective detection method for the high-dimensional nonlinear outlier samples. This paper presents an outlier detection method based on support vector machine (SVM). A SVM model built by the clean sample set without outlier is used to predict the samples, when the error between the prediction-value and actual value exceeds the threshold, the sample is taken as an outlier, otherwise a normal one. The present outlier detection method has been applied to analyze the practical copper-matte converting production data. The results show that this method can efficiently and correctly detect the high dimensional nonlinear outlier sample and has considerable practical value.
Control and Decision Conference (CCDC), 2010 Chinese
Date of Conference: 26-28 May 2010