三、议论文Since we are social beings,the quality of our livesdepends in a large measure on our interpersonalrelationships. One strength of the human condition is ourpossibility to give and receive support from one ano...三、议论文Since we are social beings,the quality of our livesdepends in a large measure on our interpersonalrelationships. One strength of the human condition is ourpossibility to give and receive support from one anotherunder stressful(有压力的)conditions.Social support展开更多
Machine Learning(ML) techniques have been widely applied in recent traffic classification.However, the problems of both discriminator bias and class imbalance decrease the accuracies of ML based traffic classifier. In...Machine Learning(ML) techniques have been widely applied in recent traffic classification.However, the problems of both discriminator bias and class imbalance decrease the accuracies of ML based traffic classifier. In this paper, we propose an accurate and extensible traffic classifier. Specifically, to address the discriminator bias issue, our classifier is built by making an optimal cascade of binary sub-classifiers, where each binary sub-classifier is trained independently with the discriminators used for identifying application specific traffic. Moreover, to balance a training dataset,we apply SMOTE algorithm in generating artificial training samples for minority classes.We evaluate our classifier on two datasets collected from different network border routers.Compared with the previous multi-class traffic classifiers built in one-time training process,our classifier achieves much higher F-Measure and AUC for each application.展开更多
文摘三、议论文Since we are social beings,the quality of our livesdepends in a large measure on our interpersonalrelationships. One strength of the human condition is ourpossibility to give and receive support from one anotherunder stressful(有压力的)conditions.Social support
基金supported by the National Natural Science Foundation of China under Grant No.61402485National Natural Science Foundation of China under Grant No.61303061supported by the Open fund from HPCL No.201513-01
文摘Machine Learning(ML) techniques have been widely applied in recent traffic classification.However, the problems of both discriminator bias and class imbalance decrease the accuracies of ML based traffic classifier. In this paper, we propose an accurate and extensible traffic classifier. Specifically, to address the discriminator bias issue, our classifier is built by making an optimal cascade of binary sub-classifiers, where each binary sub-classifier is trained independently with the discriminators used for identifying application specific traffic. Moreover, to balance a training dataset,we apply SMOTE algorithm in generating artificial training samples for minority classes.We evaluate our classifier on two datasets collected from different network border routers.Compared with the previous multi-class traffic classifiers built in one-time training process,our classifier achieves much higher F-Measure and AUC for each application.
