| 基于标准数据集的异常检测技术综述与实验分析 |
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| 引用本文： | 丁雪梅,陈贝贝.基于标准数据集的异常检测技术综述与实验分析[J].莆田高等专科学校学报,2012(2):53-60. |
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| 作者姓名： | 丁雪梅 陈贝贝 |
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| 作者单位： | 福建师范大学软件学院,福建福州350108 |
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| 基金项目： | 福建省教育厅科研基金资助项目（JA10075） |
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| 摘 要： | 为了研究目前主流的异常检测算法,并了解基于相同数据集的异常检测算法之间性能的差异,首先简要分类综述现有异常检测技术,然后着重实验分析,选取5个具有代表性的异常检测算法,应用于10组不同维数和大小的标准数据集上,执行误差性能（FNR,FPR,AUC）对比,最后试验结果表明,基于统计的高斯混合（Gaussian Mixture）算法具有较大优势。
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| 关 键 词： | 异常检测 统计方法 神经网络方法 基于规则的方法 支持向量机 |
A Review of Novelty Detection and Experimental Analysis Based on Benchmark Datasets |
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| DING Xue-mei,CHEN Bei-bei.A Review of Novelty Detection and Experimental Analysis Based on Benchmark Datasets[J].Journal of Putian College,2012(2):53-60. |
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| Authors: | DING Xue-mei CHEN Bei-bei |
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| Institution: | (Faculty of Software,Fujian Normal University,Fuzhou Fujian 350108,China) |
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| Abstract: | In order to know more about the state-of-the-art methods and to konw the differences between them,this paper firstly presents a brief taxonomy review of the existing literature on novelty detection,and then focuses on experimental analysis and performance（False Negative Rate,False Positive Rate and Area Under the Receiver Operating Characteristics Curve） comparison based on ten different benchmark datasets.Finally,it concludes that Gaussian Mixture method outperforms the other three ones,which all the five methods are selected as representative algorithms from different categories of novelty detection. |
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| Keywords: | novelty detection statistical approaches neural network approaches rule-based approach SVM approach |
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