| 低代价的数据流分类算法 |
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| 引用本文： | 李南.低代价的数据流分类算法[J].计算机系统应用,2016,25(12):187-192. |
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| 作者姓名： | 李南 |
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| 作者单位： | 福建农林大学 计算机与信息学院, 福州 350002 |
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| 基金项目： | 福建省自然科学基金（2013J01216，2016J01280） |
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| 摘 要： | 现有数据流分类算法大多使用有监督学习，而标记高速数据流上的样本需要很大的代价，因此缺乏实用性.针对以上问题，提出了一种低代价的数据流分类算法2SDC.新算法利用少量已标记类别的样本和大量未标记样本来训练和更新分类模型，并且动态监测数据流上可能发生的概念漂移.真实数据流上的实验表明，2SDC算法不仅具有和当前有监督学习分类算法相当的分类精度，并且能够自适应数据流上的概念漂移.
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| 关 键 词： | 概念漂移 数据流 分类 低代价 监督学习 |
| 收稿时间： | 2016/3/29 0:00:00 |
| 修稿时间： | 6/1/2016 12:00:00 AM |
Low-Cost Algorithm for Stream Data Classification |
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| LI Nan.Low-Cost Algorithm for Stream Data Classification[J].Computer Systems& Applications,2016,25(12):187-192. |
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| Authors: | LI Nan |
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| Affiliation: | College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China |
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| Abstract: | Existing classification algorithms for data stream are mainly based on supervised learning,while manual labeling instances arriving continuously at a high speed requires much effort.A low-cost learning algorithm for stream data classification named 2SDC is proposed to solve the problem mentioned above.With few labeled instances and a large number of unlabeled instances,2SDC trains the classification model and then updates it.The proposed algorithm can also detect the potential concept drift of the data stream and adjust the classification model to the current concept.Experimental results show that the accuracy of 2SDC is comparable to that of state-of-the-art supervised algorithm. |
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| Keywords: | concept drift data stream classification low-cost supervised learning |
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