基于支持向量机的孤立点检测方法研究
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摘要
孤立点检测是数据挖掘领域的重要内容之一。孤立点检测可以发现不具备一般数据特性的数据,进而发现潜在的有用信息。孤立点检测可以应用到很多实际领域,如信用卡欺诈检测、故障诊断、医学诊断、网络入侵检测和信息检索等。近年来很多国内外学者着力于结合支持向量机技术进行孤立点检测应用,其成果颇丰。然而随着研究的不断深入和应用范围的不断扩大,现存方法遇到了一些障碍,检测模型的泛化能力和稳定性能也存在诸多问题。由于上述原因,本文以基于支持向量机的孤立点检测为题进行研究,以期提供更加高效稳定的孤立点检测方法,主要研究内容如下:
1、一类支持向量机及其改进算法进行孤立点检测问题研究。实际应用中训练集通常包含大量的有标签正常样本,但只包含少量或者根本不存在有标签孤立点样本,这种情况下一类支持向量机表现出优势,但是由于算法对坐标原点依赖性强、参数不易选择等原因造成孤立点检测的误报率较高。针对这些问题本文首先利用受试者工作特征分析技术作为性能评价标准,使用两种参数搜索方法对模型进行优化,进而获得最佳决策函数。其次,设计了“局部密度一类支持向量机”算法,为每个样本测量数据局部密度并加到对应的松弛变量上,在训练过程中包含这些信息将有助于获得更理想的决策函数。此外,提出了“孤立点一类支持向量机”算法,通过综合距离和概率输出两种标准在无标签训练集中探测可疑孤立点,然后在特征空间刻画与可疑孤立点保持最大间隔的分类超平面,并在此基础上提出了一种根据数据异常程度动态更新数据样本的方法,提供了稳定高效的检测性能。
2、数据预处理技术改善孤立点检测中支持向量分类器性能问题研究。支持向量机进行分类操作的时候,决策超平面会受到数据库中孤立点干扰而发生偏移;其原因在于孤立点在训练过程中易于成为边界支持向量,从而对最后的决策函数做出较大贡献;另外数据维数过高也会降低分类效率和性能。为此本文提出使用数据预处理方法改善分类器性能,通过主成分分析处理训练数据,为远离聚簇中心孤立点设置较小的权值,这样孤立点对最终决策函数起到的作用将大大降低,从而缓解决策超平面被偏移的问题,提出的方法被成功地应用到蛋白质亚细胞定位预测领域。针对高维数据会影响分类器性能的问题,利用高斯过程潜变量模型来抽取特征,并且设计了阶梯跳跃式降维方法,为获得良好分类性能提供了保障。
3、使用混合策略的孤立点检测研究。孤立点检测应用中数据存在不平衡的特点,两类样本数量比例失调,将支持向量机的分类超平面向预测大类正常样本的方向倾斜,进而能够将孤立点样本全部识别为正常样本。本文首先结合两种支持向量机算法提出了一个两阶段的孤立点检测方法;集成不同权值改进半监督的一类支持向量机对数据集进行重采样,执行过程中通过设定较低权值降低孤立点的信息量,除去部分正常样本从而平衡两类样本的比例;使用代价敏感支持向量机执行孤立点检测操作,以两种误分类代价线性和最小为目标,实现了代价敏感孤立点挖掘。其次结合集成学习方法改进支持向量分类器的性能,利用聚类算法分解正常样本与孤立点样本作为单个分类器的输入,综合不同分类模型的输出结果改善孤立点检测性能。对于大类正常样本,使用聚类算法分解成多个部分,并分别计算与小类样本之间的距离,通过综合打分系统排除最远和最近的聚类;对于小类孤立点样本,使用一类支持向量机进行训练,在对应的支持向量样本上进行过采样操作;两种数据重采样方法的目的均在于平衡样本集以获得更理想的分类超平面。本文提出的混合策略方法能够提高检测率,降低误报率,同时将误分类代价降到最低。
Outlier detection refers to the problem of finding patterns in data that do not conform to expected behavior. These nonconforming patterns often imply potentially useful information. Outlier detection is one of the most important contents in the data mining community. Outlier detection finds extensive use in a wide variety of applications such as credit fraud detection, fault detection, health care, intrusion detection for network security, image retrieval. In recent years, domestic and overseas scholars are focused on applying Support Vector Machine (SVM) theory to the tasks of outlier detection, and many results have been obtained. As the research and application going, the existed methods and techniques face some difficulties on the generalization ability and robust stability of the outlier detection models. For the above observations, this dissertation will focus on SVM method and try to find new techniques for efficient and robust outlier detection based on SVMs. It covers:
1. Research on semi-supervised or unsupervised outlier detecion methods based on One-Class SVM (OCSVM). In practice, availability of labeled data for training and validation of models used by outlier detection techniques are major issues, there are only few labeled outliers in databases. One-class classification techniques are promising in detecting new outliers. However, such techniques usually gain high detection rate with high false positive rate, because proper parameters are difficult to select and the choice of origin as the separation point is arbitrary and affects the decision boundary returned by the algorithm. A new model is proposed which makes use of receiver operating characteristic (ROC) analysis technique, and the optimum parameters are automatically searched in limited scope using two techniques, then lead to the detection decision function after a boundary movement process. To identify the ideal hyperplane, a new algorithm named "local density OCSVM" is proposed by incorporating distance-based local density degree to reflect the overall characteristics of the target data. Finally, an "Outlier OCSVM" is proposed and a framework is designed for unsupervised outlier detection. Respectively scored by distance from hyper-plane and probabilistic output value, two definitions of outlier degree are presented. After picking out some suspicious outliers via combining the two criterions of outlier degree, the model starts the training operations and two parts of the data set are updated interactively through comparison of the outputs.
2. Research on robust classification models combined data preprocess techniques and SVMs in outlier detection. The experimental data sets are likely to contain outliers or noises, which can lead to poor generalization ability and classification accuracy for SVMs. This happens because the outliers may become boundary support vectors and contribute to the decision function, in addition, the high dimensional feature databases can reduce the efficiency and performance. A method using Weighted SVM (WSVM) combined with Principal Component Analysis (PCA) is then proposed for robust prediction of protein subcellular localization. After performing dimension reduction operations on the data sets, more suitable weights are generated for further training, as PCA transforms the data into a new coordinate system with largest variances affected greatly by the outliers. Gaussian process latent variable model (GPLVM) is also used for the purpose of nonlinear low dimensional embedding of sample data sets, and a new ladder jumping dimensional reduction classification framework is proposed for effectively confirming the objective dimension.
3. Research on hybrid methods for solving imbalanced classification problems in outlier detection. The data sets used in outlier detection applications are usually imbalanced, which have detrimental effects on the performance of an SVM classifier, because the classifier may be strongly biased towards the majority class. A new resampling algorithm based on a modified OCSVM is then proposed, and a two-stage outlier detection approach is designed after combining the resampling algorithm with a cost sensitive SVM. Low weights were set for outliers, and some common points were removed proportionally by the hyperplane in feature space, as could also overcome the effect of overlapping data points. The optimal parameters of the cost sensitive SVM is searched and the cumulative misclassification costs are reduced. Moreover, a new method using ensemble learning method is proposed. Both minority and majority classes are resampled to increase the generalization ability. For majority class, just instead of all data, the prototypes of the clusters are selected. In essence, this could form a way of undersampling of this class. The clusters are used to build an SVM ensemble with the oversampled minority patterns. For minority class, an OCSVM model combined with synthetic minority oversampling technique (SMOTE) is used to oversample the support vector instances. Hybrid methods adopt both strategies of modifying the data distribution and adjusting the classifier, present hight true positive rates with low false positive rates.
1、一类支持向量机及其改进算法进行孤立点检测问题研究。实际应用中训练集通常包含大量的有标签正常样本,但只包含少量或者根本不存在有标签孤立点样本,这种情况下一类支持向量机表现出优势,但是由于算法对坐标原点依赖性强、参数不易选择等原因造成孤立点检测的误报率较高。针对这些问题本文首先利用受试者工作特征分析技术作为性能评价标准,使用两种参数搜索方法对模型进行优化,进而获得最佳决策函数。其次,设计了“局部密度一类支持向量机”算法,为每个样本测量数据局部密度并加到对应的松弛变量上,在训练过程中包含这些信息将有助于获得更理想的决策函数。此外,提出了“孤立点一类支持向量机”算法,通过综合距离和概率输出两种标准在无标签训练集中探测可疑孤立点,然后在特征空间刻画与可疑孤立点保持最大间隔的分类超平面,并在此基础上提出了一种根据数据异常程度动态更新数据样本的方法,提供了稳定高效的检测性能。
2、数据预处理技术改善孤立点检测中支持向量分类器性能问题研究。支持向量机进行分类操作的时候,决策超平面会受到数据库中孤立点干扰而发生偏移;其原因在于孤立点在训练过程中易于成为边界支持向量,从而对最后的决策函数做出较大贡献;另外数据维数过高也会降低分类效率和性能。为此本文提出使用数据预处理方法改善分类器性能,通过主成分分析处理训练数据,为远离聚簇中心孤立点设置较小的权值,这样孤立点对最终决策函数起到的作用将大大降低,从而缓解决策超平面被偏移的问题,提出的方法被成功地应用到蛋白质亚细胞定位预测领域。针对高维数据会影响分类器性能的问题,利用高斯过程潜变量模型来抽取特征,并且设计了阶梯跳跃式降维方法,为获得良好分类性能提供了保障。
3、使用混合策略的孤立点检测研究。孤立点检测应用中数据存在不平衡的特点,两类样本数量比例失调,将支持向量机的分类超平面向预测大类正常样本的方向倾斜,进而能够将孤立点样本全部识别为正常样本。本文首先结合两种支持向量机算法提出了一个两阶段的孤立点检测方法;集成不同权值改进半监督的一类支持向量机对数据集进行重采样,执行过程中通过设定较低权值降低孤立点的信息量,除去部分正常样本从而平衡两类样本的比例;使用代价敏感支持向量机执行孤立点检测操作,以两种误分类代价线性和最小为目标,实现了代价敏感孤立点挖掘。其次结合集成学习方法改进支持向量分类器的性能,利用聚类算法分解正常样本与孤立点样本作为单个分类器的输入,综合不同分类模型的输出结果改善孤立点检测性能。对于大类正常样本,使用聚类算法分解成多个部分,并分别计算与小类样本之间的距离,通过综合打分系统排除最远和最近的聚类;对于小类孤立点样本,使用一类支持向量机进行训练,在对应的支持向量样本上进行过采样操作;两种数据重采样方法的目的均在于平衡样本集以获得更理想的分类超平面。本文提出的混合策略方法能够提高检测率,降低误报率,同时将误分类代价降到最低。
Outlier detection refers to the problem of finding patterns in data that do not conform to expected behavior. These nonconforming patterns often imply potentially useful information. Outlier detection is one of the most important contents in the data mining community. Outlier detection finds extensive use in a wide variety of applications such as credit fraud detection, fault detection, health care, intrusion detection for network security, image retrieval. In recent years, domestic and overseas scholars are focused on applying Support Vector Machine (SVM) theory to the tasks of outlier detection, and many results have been obtained. As the research and application going, the existed methods and techniques face some difficulties on the generalization ability and robust stability of the outlier detection models. For the above observations, this dissertation will focus on SVM method and try to find new techniques for efficient and robust outlier detection based on SVMs. It covers:
1. Research on semi-supervised or unsupervised outlier detecion methods based on One-Class SVM (OCSVM). In practice, availability of labeled data for training and validation of models used by outlier detection techniques are major issues, there are only few labeled outliers in databases. One-class classification techniques are promising in detecting new outliers. However, such techniques usually gain high detection rate with high false positive rate, because proper parameters are difficult to select and the choice of origin as the separation point is arbitrary and affects the decision boundary returned by the algorithm. A new model is proposed which makes use of receiver operating characteristic (ROC) analysis technique, and the optimum parameters are automatically searched in limited scope using two techniques, then lead to the detection decision function after a boundary movement process. To identify the ideal hyperplane, a new algorithm named "local density OCSVM" is proposed by incorporating distance-based local density degree to reflect the overall characteristics of the target data. Finally, an "Outlier OCSVM" is proposed and a framework is designed for unsupervised outlier detection. Respectively scored by distance from hyper-plane and probabilistic output value, two definitions of outlier degree are presented. After picking out some suspicious outliers via combining the two criterions of outlier degree, the model starts the training operations and two parts of the data set are updated interactively through comparison of the outputs.
2. Research on robust classification models combined data preprocess techniques and SVMs in outlier detection. The experimental data sets are likely to contain outliers or noises, which can lead to poor generalization ability and classification accuracy for SVMs. This happens because the outliers may become boundary support vectors and contribute to the decision function, in addition, the high dimensional feature databases can reduce the efficiency and performance. A method using Weighted SVM (WSVM) combined with Principal Component Analysis (PCA) is then proposed for robust prediction of protein subcellular localization. After performing dimension reduction operations on the data sets, more suitable weights are generated for further training, as PCA transforms the data into a new coordinate system with largest variances affected greatly by the outliers. Gaussian process latent variable model (GPLVM) is also used for the purpose of nonlinear low dimensional embedding of sample data sets, and a new ladder jumping dimensional reduction classification framework is proposed for effectively confirming the objective dimension.
3. Research on hybrid methods for solving imbalanced classification problems in outlier detection. The data sets used in outlier detection applications are usually imbalanced, which have detrimental effects on the performance of an SVM classifier, because the classifier may be strongly biased towards the majority class. A new resampling algorithm based on a modified OCSVM is then proposed, and a two-stage outlier detection approach is designed after combining the resampling algorithm with a cost sensitive SVM. Low weights were set for outliers, and some common points were removed proportionally by the hyperplane in feature space, as could also overcome the effect of overlapping data points. The optimal parameters of the cost sensitive SVM is searched and the cumulative misclassification costs are reduced. Moreover, a new method using ensemble learning method is proposed. Both minority and majority classes are resampled to increase the generalization ability. For majority class, just instead of all data, the prototypes of the clusters are selected. In essence, this could form a way of undersampling of this class. The clusters are used to build an SVM ensemble with the oversampled minority patterns. For minority class, an OCSVM model combined with synthetic minority oversampling technique (SMOTE) is used to oversample the support vector instances. Hybrid methods adopt both strategies of modifying the data distribution and adjusting the classifier, present hight true positive rates with low false positive rates.
引文
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