As the importance of email increases,the amount of malicious email is also increasing,so the need for malicious email filtering is growing.Since it is more economical to combine commodity hardware consisting of a medi...As the importance of email increases,the amount of malicious email is also increasing,so the need for malicious email filtering is growing.Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques,we used a Hadoop MapReduce framework and Naïve Bayes among machine learning methods for malicious email filtering.Naïve Bayes was selected because it is one of the top machine learning methods(Support Vector Machine(SVM),Naïve Bayes,K-Nearest Neighbor(KNN),and Decision Tree)in terms of execution time and accuracy.Malicious email was filtered with MapReduce programming using the Naïve Bayes technique,which is a supervised machine learning method,in a Hadoop framework with optimized performance and also with the Python program technique with the Naïve Bayes technique applied in a bare metal server environment with the Hadoop environment not applied.According to the results of a comparison of the accuracy and predictive error rates of the two methods,the Hadoop MapReduce Naïve Bayes method improved the accuracy of spam and ham email identification 1.11 times and the prediction error rate 14.13 times compared to the non-Hadoop Python Naïve Bayes method.展开更多
文章在加权线性损失函数下,基于NA样本,讨论了两参数Burr Type Ⅻ分布参数θ的经验Bayes单侧检验问题:H0:θθ0 H1:θ>θ0;利用概率密度函数的核估计和经验分布函数构造了参数的经验Bayes单侧检验函数,并获得了它的渐近最优(a.o)性...文章在加权线性损失函数下,基于NA样本,讨论了两参数Burr Type Ⅻ分布参数θ的经验Bayes单侧检验问题:H0:θθ0 H1:θ>θ0;利用概率密度函数的核估计和经验分布函数构造了参数的经验Bayes单侧检验函数,并获得了它的渐近最优(a.o)性;在适当的条件下证明了所提出的经验Bayes检验函数的收敛速度可任意接近Ο(n-1/2)。展开更多
文摘As the importance of email increases,the amount of malicious email is also increasing,so the need for malicious email filtering is growing.Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques,we used a Hadoop MapReduce framework and Naïve Bayes among machine learning methods for malicious email filtering.Naïve Bayes was selected because it is one of the top machine learning methods(Support Vector Machine(SVM),Naïve Bayes,K-Nearest Neighbor(KNN),and Decision Tree)in terms of execution time and accuracy.Malicious email was filtered with MapReduce programming using the Naïve Bayes technique,which is a supervised machine learning method,in a Hadoop framework with optimized performance and also with the Python program technique with the Naïve Bayes technique applied in a bare metal server environment with the Hadoop environment not applied.According to the results of a comparison of the accuracy and predictive error rates of the two methods,the Hadoop MapReduce Naïve Bayes method improved the accuracy of spam and ham email identification 1.11 times and the prediction error rate 14.13 times compared to the non-Hadoop Python Naïve Bayes method.
文摘文章在加权线性损失函数下,基于NA样本,讨论了两参数Burr Type Ⅻ分布参数θ的经验Bayes单侧检验问题:H0:θθ0 H1:θ>θ0;利用概率密度函数的核估计和经验分布函数构造了参数的经验Bayes单侧检验函数,并获得了它的渐近最优(a.o)性;在适当的条件下证明了所提出的经验Bayes检验函数的收敛速度可任意接近Ο(n-1/2)。
