Description: A customer dynamic evaluation method based on rough set and support vector machine is advanced. Customer evaluation indicators are analysed and established from three dimensions of current value, potential value and odditional value according to customer characteristics. The change rules of customer value are observed by annual increasing rate of indicators. Continuous attributes are discretized by rough set and boolean inference arithmetic. Redundant attributes are reduced by particle swarm optimization arithmetic. The optimal penalty factor and nuclear parameter are obtained by 10-cross validation and grid-search. The sample data scaling is carried out and the train and test of svm one against one classifier are accomplished. The result indicates that the evaluation method can not only implement the periodic evaluation and classification of customer value, but also have a better generalization.
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Custom Evaluation.doc
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