一种面向不平衡数据的结构化SVM集成分类器

为改进面向不平衡数据的SVM分类器性能,以结构化SVM为基础,提出一种基于代价敏感的结构化支持向量机集成分类器模型.该模型首先通过训练样本的聚类,得到隐含在数据中的结构信息,并对样本进行初始加权.运用AdaBoost策略对各样本的权重进行动态调整,适当增大少数类样本的权重,使小类中误分的样本代价增大,以此来改进不平衡数据的分类性能.实验结果表明,该算法可有效提高不平衡数据的分类性能.     

一种基于GMM-EM的非平衡数据的概率增强算法

非平衡数据的分类问题是机器学习领域的一个重要研究课题.在一个非平衡数据里,少数类的训练样本明显少于多数类,导致分类结果往往偏向多数类.针对非平衡数据分类问题,提出一种基于高斯混合模型-均值最大化方法(GMM-EM)的概率增强算法.首先,通过高斯混合模型(GMM)与均值最大化算法(EM)建立少数类数据的概率密度函数;其次,根据高概率密度的样本生成新样本的能力比低概率密度的样本更强的性质,建立一种基于少数类样本密度函数的过采样算法,该算法保证少数类数据集在平衡前后的概率分布的一致性,从数据集的统计性质使少数类达到平衡;最后,使用决策树分类器对已经达到平衡的数据集进行分类,并且利用评价指标对分类效果进行评判.通过从UCI和KEEL数据库选出的8组数据集的分类实验,表明了所提出算法比现有算法更有效.     

改进SVM-KNN的不平衡数据分类

针对支持向量机(SVM)在超平面附近进行不平衡数据(imbalanced datasets)分类的不准确性,提出了一种改进SVM-KNN算法,该算法在分类阶段计算测试样本与最优超平面的距离,如果距离差大于给定阈值可直接应用支持向量机分类;如果距离差小于给定阈值,则将所有支持向量都作为测试样本的近邻样本,进行KNN分类。通过对UCI数据集的大量实验表明,该算法在少数类样本的识别率和分类器的整体性能上有明显改善。     

新的基于代价敏感集成学习的非平衡数据集分类方法NIBoost

现实生活中存在大量的非平衡数据,大多数传统的分类算法假定类分布平衡或者样本的错分代价相同,因此在对这些非平衡数据进行分类时会出现少数类样本错分的问题。针对上述问题,在代价敏感的理论基础上,提出了一种新的基于代价敏感集成学习的非平衡数据分类算法--NIBoost（New Imbalanced Boost）。首先,在每次迭代过程中利用过采样算法新增一定数目的少数类样本来对数据集进行平衡,在该新数据集上训练分类器;其次,使用该分类器对数据集进行分类,并得到各样本的预测类标及该分类器的分类错误率;最后,根据分类错误率和预测的类标计算该分类器的权重系数及各样本新的权重。实验采用决策树、朴素贝叶斯作为弱分类器算法,在UCI数据集上的实验结果表明,当以决策树作为基分类器时,与RareBoost算法相比,F-value最高提高了5.91个百分点、G-mean最高提高了7.44个百分点、AUC最高提高了4.38个百分点;故该新算法在处理非平衡数据分类问题上具有一定的优势。     

基于最优间隔的AdaBoostv算法的非平衡数据分类

为了解决非平衡数据分类问题,提出了一种基于最优间隔的AdaBoostv算法.该算法采用改进的SVM作为基分类器,在SVM的优化模型中引入间隔均值项,并根据数据非平衡比对间隔均值项和损失函数项进行加权;采用带有方差减小的随机梯度方法(Stochastic Variance Reduced Gradient,SVRG)对优化模型进行求解,以加快收敛速度.所提基于最优间隔的AdaBoostv算法在样本权重更新公式中引入了一种新的自适应代价敏感函数,赋予少数类样本、误分类的少数类样本以及靠近决策边界的少数类样本更高的代价值;另外,通过结合新的权重公式以及引入给定精度参数v下的最优间隔的估计值,推导出新的基分类器权重策略,进一步提高了算法的分类精度.对比实验表明,在线性和非线性情况下,所提基于最优间隔的Ada-Boostv算法在非平衡数据集上的分类精度优于其他算法,且能获得更大的最小间隔.     

改进欠抽样方法及其在非平衡数据集分类中的应用

欠抽样方法在非平衡数据集分类时,未充分考虑数据分布变化对分类结果造成的影响。为此,提出一种基于聚类融合去冗余的改进欠抽样方法。采用聚类算法得到多数类样本高密度分布区域的聚类中心,将多数类样本划分为不同子集,通过计算各子集的相似度冗余系数对多数类样本进行去冗余删除,以达到欠抽样的目的。对15个不同平衡率的数据集欠抽样后,利用代价敏感混合属性多决策树模型进行分类。实验结果表明,在不降低非平衡数据集分类准确率的前提下,该方法能够提高少数类样本的正类率及预测模型的G-mean值。     

一种基于SMOTE的不平衡数据集重采样方法

不平衡数据集是指在数据集中,某一类样本的数量远大于其他类样本的数量,其会影响分类结果,使基本分类器偏向多数类.合成少数样本过采样技术(SMOTE)是处理数据不平衡问题的一种经典过采样方法,以两个少数样本对应的线段为端点生成一个合成样本.提出一种基于SMOTE的少数群体过采样方法,改进生成新样本的方式,在合成样本的过程中参考两个以上的少数类样本,增加合成样本的多样性.实验结果表明,在不同的基本分类器下该方法可以获得更好的接收者操作特征曲线面积(ROC-AUC)和稳定性.     

基于拆分集成的不均衡数据分类算法

为改进SVM对不均衡数据的分类性能,提出一种基于拆分集成的不均衡数据分类算法,该算法对多数类样本依据类别之间的比例通过聚类划分为多个子集,各子集分别与少数类合并成多个训练子集,通过对各训练子集进行学习获得多个分类器,利用WE集成分类器方法对多个分类器进行集成,获得最终分类器,以此改进在不均衡数据下的分类性能.在UCI数据集上的实验结果表明,该算法的有效性,特别是对少数类样本的分类性能.     

基于高斯混合模型的非平衡数据对称翻转算法

针对传统分类器对于非平衡数据的分类效果存在的问题,提出了一种基于高斯混合模型-期望最大化(GMM-EM)的对称翻转算法.该算法的核心思想是基于概率论中的"3σ法则"使数据达到平衡.首先,利用高斯混合模型和EM算法得到多数类与少数类数据的密度函数;其次,以少数类数据的均值为对称中心,根据"3σ法则"确定多数类侵入少数类的翻转边界,进行数据翻转,同时剔除与翻转区间中少数类原始数据数据重复的点;此时,若两类数据不平衡,则在翻转区域内使用概率密度增强方法使数据达到平衡.最后,从UCI、KEEL数据库中选取的14组数据使用决策树分类器对平衡后的数据进行分类,实例分析表明了该算法的有效性.     

基于聚类权重分阶段的SVM解不平衡数据集分类

SVM在处理不平衡数据分类问题（class imbalance problem）时,其分类结果常倾向于多数类。为此,综合考虑类间不平衡和类内不平衡,提出一种基于聚类权重的分阶段支持向量机（WSVM）。预处理时,采用K均值算法得到多数类中各样本的权重。分类时,第一阶段根据权重选出多数类内各簇边界区域的与少数类数目相等的样本;第二阶段对选取的样本和少数类样本进行初始分类;第三阶段用多数类中未选取的样本对初始分类器进行优化调整,当满足停止条件时,得到最终分类器。通过对UCI数据集的大量实验表明,WSVM在少数类样本的识别率和分类器的整体性能上都优于传统分类算法。     

面向不均衡数据的融合谱聚类的自适应过采样法

分类是模式识别领域中的研究热点,大多数经典的分类器往往默认数据集是分布均衡的,而现实中的数据集往往存在类别不均衡问题,即属于正常/多数类别的数据的数量与属于异常/少数类数据的数量之间的差异很大。若不对数据进行处理往往会导致分类器忽略少数类、偏向多数类,使得分类结果恶化。针对数据的不均衡分布问题,本文提出一种融合谱聚类的综合采样算法。首先采用谱聚类方法对不均衡数据集的少数类样本的分布信息进行分析,再基于分布信息对少数类样本进行过采样,获得相对均衡的样本,用于分类模型训练。在多个不均衡数据集上进行了大量实验,结果表明,所提方法能有效解决数据的不均衡问题,使得分类器对于少数类样本的分类精度得到提升。     

A multi-objective optimisation approach for class imbalance learning

Class imbalance limits the performance of most learning algorithms since they cannot cope with large differences between the number of samples in each class, resulting in a low predictive accuracy over the minority class. In this respect, several papers proposed algorithms aiming at achieving more balanced performance. However, balancing the recognition accuracies for each class very often harms the global accuracy. Indeed, in these cases the accuracy over the minority class increases while the accuracy over the majority one decreases. This paper proposes an approach to overcome this limitation: for each classification act, it chooses between the output of a classifier trained on the original skewed distribution and the output of a classifier trained according to a learning method addressing the course of imbalanced data. This choice is driven by a parameter whose value maximizes, on a validation set, two objective functions, i.e. the global accuracy and the accuracies for each class. A series of experiments on ten public datasets with different proportions between the majority and minority classes show that the proposed approach provides more balanced recognition accuracies than classifiers trained according to traditional learning methods for imbalanced data as well as larger global accuracy than classifiers trained on the original skewed distribution.     

非平衡数据集分类方法探讨

由于数据集中类分布极不平衡,很多分类算法在非平衡数据集上失效,而非平衡数据集中占少数的类在现实生活中通常具有显著意义,因此如何提高非平衡数据集中少数类的分类性能成为近年来研究的热点。详细讨论了非平衡数据集分类问题的本质、影响非平衡数据集分类的因素、非平衡数据集分类通常采用的方法、常用的评估标准以及该问题中存在的问题与挑战。     

Imbalanced classification of mental workload using a cost-sensitive majority weighted minority oversampling strategy

Identifying the temporal variations in mental workload level (MWL) is crucial for enhancing the safety of human–machine system operations, especially when there is cognitive overload or inattention of human operator. This paper proposed a cost-sensitive majority weighted minority oversampling strategy to address the imbalanced MWL data classification problem. Both the inter-class and intra-class imbalance problems are considered. For the former, imbalance ratio is defined to determine the number of the synthetic samples in the minority class. The latter problem is addressed by assigning different weights to borderline samples in the minority class based on the distance and density meaures of the sample distribution. Furthermore, multi-label classifier is designed based on an ensemble of binary classifiers. The results of analyzing 21 imbalanced UCI multi-class datasets showed that the proposed approach can effectively cope with the imbalanced classification problem in terms of several performance metrics including geometric mean (G-mean) and average accuracy (ACC). Moreover, the proposed approach was applied to the analysis of the EEG data of eight experimental participants subject to fluctuating levels of mental workload. The comparative results showed that the proposed method provides a competing alternative to several existing imbalanced learning algorithms and significantly outperforms the basic/referential method that ignores the imbalance nature of the dataset.     

Cost-sensitive decision tree ensembles for effective imbalanced classification

Real-life datasets are often imbalanced, that is, there are significantly more training samples available for some classes than for others, and consequently the conventional aim of reducing overall classification accuracy is not appropriate when dealing with such problems. Various approaches have been introduced in the literature to deal with imbalanced datasets, and are typically based on oversampling, undersampling or cost-sensitive classification. In this paper, we introduce an effective ensemble of cost-sensitive decision trees for imbalanced classification. Base classifiers are constructed according to a given cost matrix, but are trained on random feature subspaces to ensure sufficient diversity of the ensemble members. We employ an evolutionary algorithm for simultaneous classifier selection and assignment of committee member weights for the fusion process. Our proposed algorithm is evaluated on a variety of benchmark datasets, and is confirmed to lead to improved recognition of the minority class, to be capable of outperforming other state-of-the-art algorithms, and hence to represent a useful and effective approach for dealing with imbalanced datasets.     

BRACID: a comprehensive approach to learning rules from imbalanced data

In this paper we consider induction of rule-based classifiers from imbalanced data, where one class (a minority class) is under-represented in comparison to the remaining majority classes. The minority class is usually of primary interest. However, most rule-based classifiers are biased towards the majority classes and they have difficulties with correct recognition of the minority class. In this paper we discuss sources of these difficulties related to data characteristics or to an algorithm itself. Among the problems related to the data distribution we focus on the role of small disjuncts, overlapping of classes and presence of noisy examples. Then, we show that standard techniques for induction of rule-based classifiers, such as sequential covering, top-down induction of rules or classification strategies, were created with the assumption of balanced data distribution, and we explain why they are biased towards the majority classes. Some modifications of rule-based classifiers have been already introduced, but they usually concentrate on individual problems. Therefore, we propose a novel algorithm, BRACID, which more comprehensively addresses the issues associated with imbalanced data. Its main characteristics includes a hybrid representation of rules and single examples, bottom-up learning of rules and a local classification strategy using nearest rules. The usefulness of BRACID has been evaluated in experiments on several imbalanced datasets. The results show that BRACID significantly outperforms the well known rule-based classifiers C4.5rules, RIPPER, PART, CN2, MODLEM as well as other related classifiers as RISE or K-NN. Moreover, it is comparable or better than the studied approaches specialized for imbalanced data such as generalizations of rule algorithms or combinations of SMOTE + ENN preprocessing with PART. Finally, it improves the support of minority class rules, leading to better recognition of the minority class examples.     

Types of minority class examples and their influence on learning classifiers from imbalanced data

Many real-world applications reveal difficulties in learning classifiers from imbalanced data. Although several methods for improving classifiers have been introduced, the identification of conditions for the efficient use of the particular method is still an open research problem. It is also worth to study the nature of imbalanced data, characteristics of the minority class distribution and their influence on classification performance. However, current studies on imbalanced data difficulty factors have been mainly done with artificial datasets and their conclusions are not easily applicable to the real-world problems, also because the methods for their identification are not sufficiently developed. In our paper, we capture difficulties of class distribution in real datasets by considering four types of minority class examples: safe, borderline, rare and outliers. First, we confirm their occurrence in real data by exploring multidimensional visualizations of selected datasets. Then, we introduce a method for an identification of these types of examples, which is based on analyzing a class distribution in a local neighbourhood of the considered example. Two ways of modeling this neighbourhood are presented: with k-nearest examples and with kernel functions. Experiments with artificial datasets show that these methods are able to re-discover simulated types of examples. Next contributions of this paper include carrying out a comprehensive experimental study with 26 real world imbalanced datasets, where (1) we identify new data characteristics basing on the analysis of types of minority examples; (2) we demonstrate that considering the results of this analysis allow to differentiate classification performance of popular classifiers and pre-processing methods and to evaluate their areas of competence. Finally, we highlight directions of exploiting the results of our analysis for developing new algorithms for learning classifiers and pre-processing methods.     

A Dynamic Spark-based Classification Framework for Imbalanced Big Data

Classification of imbalanced big data has assembled an extensive consideration by many researchers during the last decade. Standard classification methods poorly diagnosis the minority class samples. Several approaches have been introduced for solving the problem of class imbalance in big data to enhance the generalization in classification. However, most of these approaches neglect the effect of border samples on classification performance; the high impact border samples might expose to misclassification. In this paper, a Spark Based Mining Framework (SBMF) is proposed to address the imbalanced data problem. Two main modules are designed for this purpose. The first is the Border Handling Module (BHM) which under samples the low impact majority border instances and oversamples the minority class instances. The second module is the Selective Border Instances sampling (SBI) Module, which enhances the output of the BHM module. The performance of the SBMF framework is evaluated and compared with other recent systems. A number of experiments were performed using moderate and big datasets with different imbalanced ratio. The results obtained from SBMF framework, when compared to the recent works, show better performance for the different datasets and classifiers.