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Support vector data description method with local optimization boundary
Author(s): 
Pages: 93-99
Year: Issue:  10
Journal: Electric Machines and Control

Keyword:  support vector data descriptiondecision boundarytrade-off parameterdata pre-processing;
Abstract: Conventional support vector data description ( SVDD) , which did not consider multi-modal and local distribution difference of the data, failed to reflect time-space variety rule of the object and hard to gain the optimal decision boundary. To solve this difficulty, a new SVDD method with local optimization boundary ( LOB-SVDD) was proposed. First, the local dispersion degree of each data point was calculat-ed, then, the coefficient of trade-off parameters was adjusted with the local dispersion degree, finally, the quadratic programming problem was solved and an optimized boundary function was obtained. The method can be used in data classification, outlier detection and data modeling, etc. Experiments with UCI datasets and artificial dual mode datasets show that the method can gain a more optimal decision boundary compared to the conventional method, and as classifier it can gain lower false positives rate and false nega-tives rate. That method was applied to the multi-modal actual production data of copper matte converting process, and the results show that it can effectively detect outliers, eliminate abnormal sample data.
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