| A semi-random multiple decision-tree algorithm for mining data streams |
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| 作者姓名： | Xue-Gang Hu Pei-Pei Li Xin-Dong Wu and Gong-Qing Wu |
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| 作者单位： | School of Computer Science and Information Engineering Hefei University of Technology Hefei 230009，China，School of Computer Science and Information Engineering Hefei University of Technology，Hefei 230009，China，School of Computer Science and Information Engineering Hefei University of Technology，Hefei 230009，China Department of Computer Science，University of Vermont，Burlington，VT 50405，U.S.A.，School of Computer Science and Information Engineering Hefei University of Technology，Hefei 230009，China |
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| 基金项目： | This research is supported by the National Natural Science Foundation of China(Grant No.60573174)，the Natural Science Foundation of Anhui Province of China(Grant No.050420207). |
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| 摘 要： | Mining with streaming data is a hot topic in data mining. When performing classification on data streams, traditional classification algorithms based on decision trees, such as ID3 and C4.5, have a relatively poor efficiency in both time and space due to the characteristics of streaming data. There are some advantages in time and space when using random decision trees. An incremental algorithm for mining data streams, SRMTDS （Semi-Random Multiple decision Trees for Data Streams）, based on random decision trees is proposed in this paper. SRMTDS uses the inequality of Hoeffding bounds to choose the minimum number of split-examples, a heuristic method to compute the information gain for obtaining the split thresholds of numerical attributes, and a Naive Bayes classifier to estimate the class labels of tree leaves. Our extensive experimental study shows that SRMTDS has an improved performance in time, space, accuracy and the anti-noise capability in comparison with VFDTc, a state-of-the-art decision-tree algorithm for classifying data streams.
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| 关 键 词： | 随机决策树 数据流 数据采集 分类算法 |
| 修稿时间： | 2005-01-15 |
A Semi-Random Multiple Decision-Tree Algorithm for Mining Data Streams |
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| Xue-Gang Hu,Pei-Pei Li,Xin-Dong Wu,and Gong-Qing Wu.A semi-random multiple decision-tree algorithm for mining data streams[J].Journal of Computer Science and Technology,2007,22(5):711-724. |
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| Authors: | Xue-Gang Hu Pei-Pei Li Xin-Dong Wu Gong-Qing Wu |
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| Affiliation: | 1.School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China ;2.Department of Computer Science, University of Vermont, Burlington, VT 50405, U.S.A. |
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| Abstract: | Mining with streaming data is a hot topic in data mining.When performing classification on data streams, traditional classification algorithms based on decision trees,such as ID3 and C4.5,have a relatively poor efficiency in both time and space due to the characteristics of streaming data.There are some advantages in time and space when using random decision trees.An incremental algorithm for mining data streams,SRMTDS(Semi-Random Multiple decision Trees for Data Streams),based on random decision trees is proposed in this paper.SRMTDS uses the inequality of Hoeffding bounds to choose the minimum number of split-examples,a heuristic method to compute the information gain for obtaining the split thresholds of numerical attributes,and a Naive Bayes classifier to estimate the class labels of tree leaves.Our extensive experimental study shows that SRMTDS has an improved performance in time,space,accuracy and the anti-noise capability in comparison with VFDTc,a state-of-the-art decision-tree algorithm for classifying data streams. |
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| Keywords: | data streams Naive Bayes random decision trees |
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