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In the traditional AdaBoost method,the weights are updated based on the weak classifier's overall error rate, this article proposes one kind of the method which unifies the level of difference between the threshold and the feature value of the weak classifier with the weak classifier's overall error rate. Compared to the method which only based on the overall classification error rate to update the weights,this method can achieve higher detection rate while reduces the mistaken-detection rate.

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Methods The platelet concentrates were collected by the Amicus with the Version 2.5 software (Baxter-Fenwal).

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In this models, first we extract features and rules fromthe training data, then using these rules to detect new intrusion, by using this means, updating rules and system will be more faster and cheaper, and the detection rate is high, the result proves that using data mining technology for building Intrusion Detection System is viably and availably.

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