Based on the framework of support vector machines( SVM) using one-against-one( OAO) strategy, a new multi-class kernel method based on directed acyclic graph( DAG) and probabilistic distance is proposed to raise the m...Based on the framework of support vector machines( SVM) using one-against-one( OAO) strategy, a new multi-class kernel method based on directed acyclic graph( DAG) and probabilistic distance is proposed to raise the multi-class classification accuracies. The topology structure of DAG is constructed by rearranging the nodes' sequence in the graph. DAG is equivalent to guided operating SVM on a list,and the classification performance depends on the nodes' sequence in the graph. Jeffries-Matusita distance( JMD) is introduced to estimate the separability of each class,and the implementation list is initialized with all classes organized according to certain sequence in the list. To testify the effectiveness of the proposed method,numerical analysis is conducted on UCI data and hyperspectral data. Meanwhile,comparative studies using standard OAO and DAG classification methods are also conducted and the results illustrate better performance and higher accuracy of the proposed JMD-DAG method.展开更多
Machine learning has a powerful potential for performing the template attack(TA) of cryptographic device. To improve the accuracy and time consuming of electromagnetic template attack(ETA), a multi-class directed acyc...Machine learning has a powerful potential for performing the template attack(TA) of cryptographic device. To improve the accuracy and time consuming of electromagnetic template attack(ETA), a multi-class directed acyclic graph support vector machine(DAGSVM) method is proposed to predict the Hamming weight of the key. The method needs to generate K(K ? 1)/2 binary support vector machine(SVM) classifiers and realizes the K-class prediction using a rooted binary directed acyclic graph(DAG) testing model. Further, particle swarm optimization(PSO) is used for optimal selection of DAGSVM model parameters to improve the performance of DAGSVM. By exploiting the electromagnetic emanations captured while a chip was implementing the RC4 algorithm in software, the computation complexity and performance of several multi-class machine learning methods, such as DAGSVM, one-versus-one(OVO)SVM, one-versus-all(OVA)SVM, Probabilistic neural networks(PNN), K-means clustering and fuzzy neural network(FNN) are investigated. In the same scenario, the highest classification accuracy of Hamming weight for the key reached 100%, 95.33%, 85%, 74%, 49.67% and 38% for DAGSVM, OVOSVM, OVASVM, PNN, K-means and FNN, respectively. The experiment results demonstrate the proposed model performs higher predictive accuracy and faster convergence speed.展开更多
针对多分类支持向量机算法中的低效问题和样本不平衡问题,提出一种有向无环图-双支持向量机DAG-TWSVM(directed acyclic graph and twin support vector machine)的多分类方法。该算法综合了双支持向量机和有向无环图支持向量机的优势,...针对多分类支持向量机算法中的低效问题和样本不平衡问题,提出一种有向无环图-双支持向量机DAG-TWSVM(directed acyclic graph and twin support vector machine)的多分类方法。该算法综合了双支持向量机和有向无环图支持向量机的优势,使其不仅能够得到较好的分类精度,同时还能够大大缩减训练时间。在处理较大规模数据集多分类问题时,其时间优势更为突出。采用UCI(University of California Irvine)机器学习数据库和Statlog数据库对该算法进行验证,实验结果表明,有向无环图-双支持向量机多分类方法在训练时间上较其他多分类支持向量机大大缩短,且在样本不平衡时的分类性能要优于其他多分类支持向量机,同时解决了经典支持向量机一对一多分类算法可能存在的不可分区域问题。展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.61201310)the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.201160)the China Postdoctoral Science Foundation(Grant No.20110491067)
文摘Based on the framework of support vector machines( SVM) using one-against-one( OAO) strategy, a new multi-class kernel method based on directed acyclic graph( DAG) and probabilistic distance is proposed to raise the multi-class classification accuracies. The topology structure of DAG is constructed by rearranging the nodes' sequence in the graph. DAG is equivalent to guided operating SVM on a list,and the classification performance depends on the nodes' sequence in the graph. Jeffries-Matusita distance( JMD) is introduced to estimate the separability of each class,and the implementation list is initialized with all classes organized according to certain sequence in the list. To testify the effectiveness of the proposed method,numerical analysis is conducted on UCI data and hyperspectral data. Meanwhile,comparative studies using standard OAO and DAG classification methods are also conducted and the results illustrate better performance and higher accuracy of the proposed JMD-DAG method.
基金supported by the National Natural Science Foundation of China(61571063,61202399,61171051)
文摘Machine learning has a powerful potential for performing the template attack(TA) of cryptographic device. To improve the accuracy and time consuming of electromagnetic template attack(ETA), a multi-class directed acyclic graph support vector machine(DAGSVM) method is proposed to predict the Hamming weight of the key. The method needs to generate K(K ? 1)/2 binary support vector machine(SVM) classifiers and realizes the K-class prediction using a rooted binary directed acyclic graph(DAG) testing model. Further, particle swarm optimization(PSO) is used for optimal selection of DAGSVM model parameters to improve the performance of DAGSVM. By exploiting the electromagnetic emanations captured while a chip was implementing the RC4 algorithm in software, the computation complexity and performance of several multi-class machine learning methods, such as DAGSVM, one-versus-one(OVO)SVM, one-versus-all(OVA)SVM, Probabilistic neural networks(PNN), K-means clustering and fuzzy neural network(FNN) are investigated. In the same scenario, the highest classification accuracy of Hamming weight for the key reached 100%, 95.33%, 85%, 74%, 49.67% and 38% for DAGSVM, OVOSVM, OVASVM, PNN, K-means and FNN, respectively. The experiment results demonstrate the proposed model performs higher predictive accuracy and faster convergence speed.
文摘针对多分类支持向量机算法中的低效问题和样本不平衡问题,提出一种有向无环图-双支持向量机DAG-TWSVM(directed acyclic graph and twin support vector machine)的多分类方法。该算法综合了双支持向量机和有向无环图支持向量机的优势,使其不仅能够得到较好的分类精度,同时还能够大大缩减训练时间。在处理较大规模数据集多分类问题时,其时间优势更为突出。采用UCI(University of California Irvine)机器学习数据库和Statlog数据库对该算法进行验证,实验结果表明,有向无环图-双支持向量机多分类方法在训练时间上较其他多分类支持向量机大大缩短,且在样本不平衡时的分类性能要优于其他多分类支持向量机,同时解决了经典支持向量机一对一多分类算法可能存在的不可分区域问题。
