Classification can be regarded as dividing the data space into decision regions separated by decision boundaries.In this paper we analyze decision tree algorithms and the NBTree algorithm from this perspective.Thus,a ...Classification can be regarded as dividing the data space into decision regions separated by decision boundaries.In this paper we analyze decision tree algorithms and the NBTree algorithm from this perspective.Thus,a decision tree can be regarded as a classifier tree,in which each classifier on a non-root node is trained in decision regions of the classifier on the parent node.Meanwhile,the NBTree algorithm,which generates a classifier tree with the C4.5 algorithm and the naive Bayes classifier as the root and leaf classifiers respectively,can also be regarded as training naive Bayes classifiers in decision regions of the C4.5 algorithm.We propose a second division (SD) algorithm and three soft second division (SD-soft) algorithms to train classifiers in decision regions of the naive Bayes classifier.These four novel algorithms all generate two-level classifier trees with the naive Bayes classifier as root classifiers.The SD and three SD-soft algorithms can make good use of both the information contained in instances near decision boundaries,and those that may be ignored by the naive Bayes classifier.Finally,we conduct experiments on 30 data sets from the UC Irvine (UCI) repository.Experiment results show that the SD algorithm can obtain better generali-zation abilities than the NBTree and the averaged one-dependence estimators (AODE) algorithms when using the C4.5 algorithm and support vector machine (SVM) as leaf classifiers.Further experiments indicate that our three SD-soft algorithms can achieve better generalization abilities than the SD algorithm when argument values are selected appropriately.展开更多
The objective of this study is to investigate the effectiveness of a multi-parameter intelligent fault diagnosis method based on a modified random forest algorithm(RFNB algorithm),so as to reduce the impact of blockag...The objective of this study is to investigate the effectiveness of a multi-parameter intelligent fault diagnosis method based on a modified random forest algorithm(RFNB algorithm),so as to reduce the impact of blockage fault on the operation of a silage harvester,thus providing a reference for the intelligent control.In brief,the forward speed,cutting speed,engine speed and engine load were selected as the input variables.Then,a random forest(RF)was used to construct a naive Bayes classifier for each node of the decision tree,and finally the RFNB algorithm constituted based on the naive Bayes tree(NBTree).The results revealed that by improving the classification accuracy of a single decision tree,the fault diagnosis accuracy of the entire RF was improved.When the sample data were consistent,the accuracy of the RFNB algorithm was 97.9%,while that of the RF algorithm was only 93.27%.Besides,the performance of RFNB classifiers was significantly better than that of RF classifiers.In conclusion,the RFNB model can accurately identify the fault status of the silage harvester with its good robustness,which provides a new idea for the fault monitoring and early warning of large agricultural rotating machinery in the future.展开更多
基金supported by the National Natural Science Foundation of China (No.60970081)the National Basic Research Program (973) of China (No.2010CB327903)
文摘Classification can be regarded as dividing the data space into decision regions separated by decision boundaries.In this paper we analyze decision tree algorithms and the NBTree algorithm from this perspective.Thus,a decision tree can be regarded as a classifier tree,in which each classifier on a non-root node is trained in decision regions of the classifier on the parent node.Meanwhile,the NBTree algorithm,which generates a classifier tree with the C4.5 algorithm and the naive Bayes classifier as the root and leaf classifiers respectively,can also be regarded as training naive Bayes classifiers in decision regions of the C4.5 algorithm.We propose a second division (SD) algorithm and three soft second division (SD-soft) algorithms to train classifiers in decision regions of the naive Bayes classifier.These four novel algorithms all generate two-level classifier trees with the naive Bayes classifier as root classifiers.The SD and three SD-soft algorithms can make good use of both the information contained in instances near decision boundaries,and those that may be ignored by the naive Bayes classifier.Finally,we conduct experiments on 30 data sets from the UC Irvine (UCI) repository.Experiment results show that the SD algorithm can obtain better generali-zation abilities than the NBTree and the averaged one-dependence estimators (AODE) algorithms when using the C4.5 algorithm and support vector machine (SVM) as leaf classifiers.Further experiments indicate that our three SD-soft algorithms can achieve better generalization abilities than the SD algorithm when argument values are selected appropriately.
基金National Key R&D Program of China(2016YFD0701301)。
文摘The objective of this study is to investigate the effectiveness of a multi-parameter intelligent fault diagnosis method based on a modified random forest algorithm(RFNB algorithm),so as to reduce the impact of blockage fault on the operation of a silage harvester,thus providing a reference for the intelligent control.In brief,the forward speed,cutting speed,engine speed and engine load were selected as the input variables.Then,a random forest(RF)was used to construct a naive Bayes classifier for each node of the decision tree,and finally the RFNB algorithm constituted based on the naive Bayes tree(NBTree).The results revealed that by improving the classification accuracy of a single decision tree,the fault diagnosis accuracy of the entire RF was improved.When the sample data were consistent,the accuracy of the RFNB algorithm was 97.9%,while that of the RF algorithm was only 93.27%.Besides,the performance of RFNB classifiers was significantly better than that of RF classifiers.In conclusion,the RFNB model can accurately identify the fault status of the silage harvester with its good robustness,which provides a new idea for the fault monitoring and early warning of large agricultural rotating machinery in the future.
