Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2020-09-28 Cooperative journals: 《计算机应用研究》
Abstract： This paper developed a causality modeling algorithm based on maximum information transfer entropy to solve the problem that traditional causality algorithms were difficult to accurately analyze non-linear data with a lot of noise. First, used the maximum information coefficient to detect the correlation between time series trends of non-linear data. Weaken the effect of noise on the correlation between variables. Secondly, eliminated weakly related variables based on screening factors. Calculated the transfer entropy between strong correlations using stochastic empirical valuation. Thereby reducing the calculation amount of transfer entropy. Finally, transfer entropy determined causal direction. Formed a one-way causal network that supports link traceability. Test analysis of the algorithm using classic chemical process data sets. Test results show that, compared to existing algorithms, this algorithm can locate abnormal variables. The stability of this algorithm for modeling high-dimensional data of more than 12 dimensions is higher than 85%, and the accuracy rate of causality can reach 83.33%. The actual modeling effect of this algorithm is better than the comparison algorithms, and it can detect and locate industrial control system abnormalities.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-12 Cooperative journals: 《计算机应用研究》
Abstract： The existing AdaBoost. M2 algorithm are insufficient in protocol traffic multiclass imbalance to solve the problem. So, this thesis proposes an ensemble algorithom called RBWS-ADAM2 for the classification of multiclass internet traffic. During each iteration of AdaBoost. M2, this algorithm preprocessed the training dataset by randomly balanced resampling, this strategy changed the number of majorities and minorities by randomly setting the sampling balance point to build multiple different training datasets. Moreover, this strategy toke sample weight as the basis for sample screening to strengthen the learning of this kind of sample. The experimental comparison of RBWS-ADAM2 algorithm and other similar algorithms on the internationally published protocol traffic datasets shows that, compared to other algorithms, the proposed RBWS-ADAM2 algorithm not only improves the F-Measure of most minorities, but increases the overall G-mean and the overall average F-measure effectively, and obviously enhances the overall performance of the ensemble classifier.
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