In communication alarm correlation analysis,traditional association rules generation(ARG) algorithm usually has low efficiency and high error rate.This paper proposes an alarm correlation rules generation algorithm ba...In communication alarm correlation analysis,traditional association rules generation(ARG) algorithm usually has low efficiency and high error rate.This paper proposes an alarm correlation rules generation algorithm based on the confidence covered value.Confidence covered value method can judge whether a rule is redundant or not scientific After the rules that based on weighted frequent patterns(WFPs) generated,the association rules were deleted by the confidence covered value,in order to delete the redundant rules and keep the rules with more information.Experiments show that the alarm correlation rules generation algorithm based on the confidence covered value has higher efficiency than the traditional method,and can effectively remove redundant rules.Thus it is very suitable for telecommunication alarm association rules processing.展开更多
Many supervised classification algorithms have been proposed, however, they are rarely evaluated for specific application. This research examines the performance of machine learning classifiers support vector machine ...Many supervised classification algorithms have been proposed, however, they are rarely evaluated for specific application. This research examines the performance of machine learning classifiers support vector machine (SVM), neural network (NN), Random Forest (RF) against maximum classifier (MLC) (traditional supervised classifier) in forest resources and land cover categorization, based on combination of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and Landsat Thematic Mapper (TM) data, in Northern Tanzania. Various data categories based on Landsat TM surface reflectance, ALOS PALSAR backscattering and their derivatives were generated for various classification scenarios. Then a separate and joint processing of Landsat and ALOS PALSAR data were executed using SVM, NN, RF and ML classifiers. The overall classification accuracy (OA), kappa coefficient (KC) and F1 score index values were computed. The result proves the robustness of SVM and RF in classification of forest resource and land cover using mere Landsat data and integration of Landsat and PALSAR (average OA = 92% and F1 = 0.7 to 1). A two sample t-statistics was utilized to evaluate the performance of the classifiers using different data categories. SVM and RF indicate there is no significance difference at 5% significance level. SVM and RF show a significant difference when compared to NN and ML. Generally, the study suggests that parametric classifiers indicate better performance compared to parametric classifier.展开更多
基金Project of Sichuan Provincial Department of Education,China(No.13Z215)the Foundation of Scientific Research of Chengdu University of Information Technology,China(No.J201405)+1 种基金the Project of Sichuan Provincial Department of Science and Technology,China(No.2015JY0047)the Open Research Subject of Key Laboratory of Signal and Information Processing,China(No.szjj 2015-070)
文摘In communication alarm correlation analysis,traditional association rules generation(ARG) algorithm usually has low efficiency and high error rate.This paper proposes an alarm correlation rules generation algorithm based on the confidence covered value.Confidence covered value method can judge whether a rule is redundant or not scientific After the rules that based on weighted frequent patterns(WFPs) generated,the association rules were deleted by the confidence covered value,in order to delete the redundant rules and keep the rules with more information.Experiments show that the alarm correlation rules generation algorithm based on the confidence covered value has higher efficiency than the traditional method,and can effectively remove redundant rules.Thus it is very suitable for telecommunication alarm association rules processing.
文摘Many supervised classification algorithms have been proposed, however, they are rarely evaluated for specific application. This research examines the performance of machine learning classifiers support vector machine (SVM), neural network (NN), Random Forest (RF) against maximum classifier (MLC) (traditional supervised classifier) in forest resources and land cover categorization, based on combination of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and Landsat Thematic Mapper (TM) data, in Northern Tanzania. Various data categories based on Landsat TM surface reflectance, ALOS PALSAR backscattering and their derivatives were generated for various classification scenarios. Then a separate and joint processing of Landsat and ALOS PALSAR data were executed using SVM, NN, RF and ML classifiers. The overall classification accuracy (OA), kappa coefficient (KC) and F1 score index values were computed. The result proves the robustness of SVM and RF in classification of forest resource and land cover using mere Landsat data and integration of Landsat and PALSAR (average OA = 92% and F1 = 0.7 to 1). A two sample t-statistics was utilized to evaluate the performance of the classifiers using different data categories. SVM and RF indicate there is no significance difference at 5% significance level. SVM and RF show a significant difference when compared to NN and ML. Generally, the study suggests that parametric classifiers indicate better performance compared to parametric classifier.
