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Support Vector Machines are the state-of-the-art tools in data mining. However, their strength are also their main weakness, as the generated nonlinear models are typically regarded as incomprehensible black-box models. Therefore, opening the black-boxor making SVMs explainable became more important and necessary in areas such as medical diagnosis and credit evaluation. Rule extraction from SVMs,...
Support Vector Machines have been promising tools for data mining during these years because of their good performance. However, a main weakness of SVMs is lack of comprehensibility: people can not understand what the “optimal hyperplane” means and are unconfident about the prediction especially when they are not the domain experts. In this paper we introduce a new method to extract knowledge with...
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