多类不平衡数据分类方法综述

现实中许多领域产生的数据通常具有多个类别并且是不平衡的。在多类不平衡分类中,类重叠、噪声和多个少数类等问题降低了分类器的能力,而有效解决多类不平衡问题已经成为机器学习与数据挖掘领域中重要的研究课题。根据近年来的多类不平衡分类方法的文献,从数据预处理和算法级分类方法两方面进行了分析与总结,并从优缺点和数据集等方面对所有算法进行了详细的分析。在数据预处理方法中,介绍了过采样、欠采样、混合采样和特征选择方法,对使用相同数据集算法的性能进行了比较。从基分类器优化、集成学习和多类分解技术三个方面对算法级分类方法展开介绍和分析。最后对多类不平衡数据分类研究领域的未来发展方向进行总结归纳。     

SMOTE-RSB *: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory

Imbalanced data is a common problem in classification. This phenomenon is growing in importance since it appears in most real domains. It has special relevance to highly imbalanced data-sets (when the ratio between classes is high). Many techniques have been developed to tackle the problem of imbalanced training sets in supervised learning. Such techniques have been divided into two large groups: those at the algorithm level and those at the data level. Data level groups that have been emphasized are those that try to balance the training sets by reducing the larger class through the elimination of samples or increasing the smaller one by constructing new samples, known as undersampling and oversampling, respectively. This paper proposes a new hybrid method for preprocessing imbalanced data-sets through the construction of new samples, using the Synthetic Minority Oversampling Technique together with the application of an editing technique based on the Rough Set Theory and the lower approximation of a subset. The proposed method has been validated by an experimental study showing good results using C4.5 as the learning algorithm.     

Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems

Classification in imbalanced domains is a recent challenge in data mining. We refer to imbalanced classification when data presents many examples from one class and few from the other class, and the less representative class is the one which has more interest from the point of view of the learning task. One of the most used techniques to tackle this problem consists in preprocessing the data previously to the learning process. This preprocessing could be done through under-sampling; removing examples, mainly belonging to the majority class; and over-sampling, by means of replicating or generating new minority examples. In this paper, we propose an under-sampling procedure guided by evolutionary algorithms to perform a training set selection for enhancing the decision trees obtained by the C4.5 algorithm and the rule sets obtained by PART rule induction algorithm. The proposal has been compared with other under-sampling and over-sampling techniques and the results indicate that the new approach is very competitive in terms of accuracy when comparing with over-sampling and it outperforms standard under-sampling. Moreover, the obtained models are smaller in terms of number of leaves or rules generated and they can considered more interpretable. The results have been contrasted through non-parametric statistical tests over multiple data sets.     

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.     

基于海林格距离和SMOTE的多类不平衡学习算法

数据不平衡现象在现实生活中普遍存在。在处理不平衡数据时,传统的机器学习算法难以达到令人满意的效果。少数类样本合成上采样技术(Synthetic Minority Oversampling Technique,SMOTE)是一种有效的方法,但在多类不平衡数据中,边界点分布错乱和类别分布不连续变得更加复杂,导致合成的样本点会侵入其他类别区域,造成数据过泛化。鉴于基于海林格距离的决策树已被证明对不平衡数据具有不敏感性,文中结合海林格距离和SMOTE,提出了一种基于海林格距离和SMOTE的上采样算法(Based on Hellinger Distance and SMOTE Oversampling Algorithm,HDSMOTE)。首先,建立基于海林格距离的采样方向选择策略,通过比较少数类样本点的局部近邻域内的海林格距离的大小,来引导合成样本点的方向。其次,设计了基于海林格距离的采样质量评估策略,以免合成的样本点侵入其他类别的区域,降低过泛化的风险。最后,采用7种代表性的上采样算法和HDSMOTE算法对15个多类不平衡数据集进行预处理,使用决策树的分类器进行分类,以Precision,Recall,F-measure,G-mean和MAUC作为评价标准对各算法的性能进行评价。实验结果表明,相比于对比算法,HDSMOTE算法在以上评价标准上均有所提升:在Precision上最高提升了17.07%,在Recall上最高提升了21.74%,在F-measure上最高提升了19.63%,在G-mean上最高提升了16.37%,在MAUC上最高提升了8.51%。HDSMOTE相对于7种代表性的上采样方法,在处理多类不平衡数据时有更好的分类效果。     

Synthetic minority oversampling for function approximation problems

Imbalanced data sets are a common occurrence in important machine learning problems. Research in improving learning under imbalanced conditions has largely focused on classification problems (ie, problems with a categorical dependent variable). However, imbalanced data also occur in function approximation, and far less attention has been paid to this case. We present a novel stratification approach for imbalanced function approximation problems. Our solution extends the SMOTE oversampling preprocessing technique to continuous-valued dependent variables by identifying regions of the feature space with a low density of examples and high variance in the dependent variable. Synthetic examples are then generated between nearest neighbors in these regions. In an empirical validation, our approach reduces the normalized mean-squared prediction error in 18 out of 21 benchmark data sets, and compares favorably with state-of-the-art approaches.     

一种改进的降噪自编码神经网络不平衡数据分类算法

针对少数类样本合成过采样技术(Synthetic Minority Over-Sampling Technique, SMOTE)在合成少数类新样本时会带来噪音问题,提出了一种改进降噪自编码神经网络不平衡数据分类算法(SMOTE-SDAE)。该算法首先通过SMOTE方法合成少数类新样本以均衡原始数据集,考虑到合成样本过程中会产生噪音的影响,利用降噪自编码神经网络算法的逐层无监督降噪学习和有监督微调过程,有效实现对过采样数据集的降噪处理与数据分类。在UCI不平衡数据集上实验结果表明,相比传统SVM算法,该算法显著提高了不平衡数据集中少数类的分类精度。     

重采样在微博机器人识别中的应用研究

随着微博机器人账户的不断增多,对其识别检测已成为当前数据挖掘领域的热点问题。已有的微博机器人识别研究多使用爬取搜集的相关数据,在小规模平衡分布的机器人与普通用户数据集上训练并验证算法模型, 在样本分布不平衡的真实情况下存在局限性。重采样是一种针对不平衡数据集分类的常用技术,为探究重采样对 相关监督学习机器人识别算法的影响,该文以微热点数据挖掘竞赛的真实数据为基础,提出一种结合重采样的微 博机器人识别框架,在5种不同采样方式的基础上使用多种评价指标,综合评估了7种监督学习算法在不平衡验 证集上的分类性能。实验结果表明,以往基于小规模平衡样本数据训练的模型在真实情况下的Recall有较大降低,而结合重采样的算法框架能够大幅提高机器人账户的识别率,其中使用 NearMiss欠采样会让算法的 Recall大幅提升,而使用 ADASYN 过采样会让算法的 G_mean有所提高。一般而言,微博用户的发布时间、发布地域以及 发布时间间隔等属性是区分正常用户和机器人的重要特征属性。重采样调整了机器学习算法所依赖的特征属性, 从而获得更好的预测性能。     

Training cost-sensitive neural networks with methods addressing the class imbalance problem

This paper studies empirically the effect of sampling and threshold-moving in training cost-sensitive neural networks. Both oversampling and undersampling are considered. These techniques modify the distribution of the training data such that the costs of the examples are conveyed explicitly by the appearances of the examples. Threshold-moving tries to move the output threshold toward inexpensive classes such that examples with higher costs become harder to be misclassified. Moreover, hard-ensemble and soft-ensemble, i.e., the combination of above techniques via hard or soft voting schemes, are also tested. Twenty-one UCl data sets with three types of cost matrices and a real-world cost-sensitive data set are used in the empirical study. The results suggest that cost-sensitive learning with multiclass tasks is more difficult than with two-class tasks, and a higher degree of class imbalance may increase the difficulty. It also reveals that almost all the techniques are effective on two-class tasks, while most are ineffective and even may cause negative effect on multiclass tasks. Overall, threshold-moving and soft-ensemble are relatively good choices in training cost-sensitive neural networks. The empirical study also suggests that some methods that have been believed to be effective in addressing the class imbalance problem may, in fact, only be effective on learning with imbalanced two-class data sets.     

嵌入重采样技术的C4.5决策树集成分类算法的临床医学预测

决策树作为一种经典的分类算法,因其分类规则简单易懂被广泛应用于医学数据分析中.然而,医学数据的样本不平衡问题使得决策树算法的分类效果降低.数据重采样是目前解决样本不平衡问题的常见方法,通过改变样本分布提升少数类样本的分类性能.现有重采样方法往往独立于后续学习算法,采样后的数据对于弱分类器的构建不一定有效.鉴于此,提出一种基于C4.5算法的混合采样算法.该算法以C4.5算法为迭代采样的评价准则控制过采样和欠采样的迭代过程,同时依据数据的不平衡比动态更新过采样的采样倍率,最终以投票机制组合多个弱分类器预测结果.通过在9组UCI数据集上的对比实验,表明所提出算法的有效性,同时算法也在稽留流产数据上实现了准确的预测.     

A Multiple Resampling Method for Learning from Imbalanced Data Sets

Resampling methods are commonly used for dealing with the class-imbalance problem. Their advantage over other methods is that they are external and thus, easily transportable. Although such approaches can be very simple to implement, tuning them most effectively is not an easy task. In particular, it is unclear whether oversampling is more effective than undersampling and which oversampling or undersampling rate should be used. This paper presents an experimental study of these questions and concludes that combining different expressions of the resampling approach is an effective solution to the tuning problem. The proposed combination scheme is evaluated on imbalanced subsets of the Reuters-21578 text collection and is shown to be quite effective for these problems.     

HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques

Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real-world applications. Among a series of diverse techniques to solve this problem, sampling algorithms are regarded as an efficient solution. However, the problem is more fundamental, with many works emphasizing the importance of instance hardness. This issue refers to the significance of managing unsafe or potentially noisy instances that are more likely to be misclassified and serve as the root cause of poor classification performance. This paper introduces HardVis, a visual analytics system designed to handle instance hardness mainly in imbalanced classification scenarios. Our proposed system assists users in visually comparing different distributions of data types, selecting types of instances based on local characteristics that will later be affected by the active sampling method, and validating which suggestions from undersampling or oversampling techniques are beneficial for the ML model. Additionally, rather than uniformly undersampling/oversampling a specific class, we allow users to find and sample easy and difficult to classify training instances from all classes. Users can explore subsets of data from different perspectives to decide all those parameters, while HardVis keeps track of their steps and evaluates the model's predictive performance in a test set separately. The end result is a well-balanced data set that boosts the predictive power of the ML model. The efficacy and effectiveness of HardVis are demonstrated with a hypothetical usage scenario and a use case. Finally, we also look at how useful our system is based on feedback we received from ML experts.     

Credit scoring for a microcredit data set using the synthetic minority oversampling technique and ensemble classifiers

Although microfinance organizations play an important role in developing economies, decision support models for microfinance credit scoring have not been sufficiently covered in the literature, particularly for microcredit enterprises. The aim of this paper is to create a three‐class model that can improve credit risk assessment in the microfinance context. The real‐world microcredit data set used in this study includes data from retail, micro, and small enterprises. To the best of the authors' knowledge, existing research on microfinance credit scoring has been limited to regression and genetic algorithms, thereby excluding novel machine learning algorithms. The aim of this research is to close this gap. The proposed models predict default events by analysing different ensemble classification methods that empower the effects of the synthetic minority oversampling technique (SMOTE) used in the preprocessing of the imbalanced microcredit data set. Initial results have shown improvement in the prediction results for certain classes when the oversampling technique with homogeneous and heterogeneous ensemble classifier methods was applied. A prediction improvement for all classes was achieved via application of SMOTE and the Consolidated Trees Construction algorithm together with Rotation Forest. To obtain a complete view of all aspects, an additional set of metrics is used in the evaluation of performance.     

融合过抽样和欠抽样的不平衡数据重抽样方法

在机器学习领域的研究当中,分类器的性能会受到许多方面的影响,其中训练数据的不平衡对分类器的影响尤为严重。训练数据的不平衡也就是指在提供的训练数据集中,一类的样本总教远多于另一类的样本总数。常用的不平衡数据的处理方法有很多,只探讨利用重抽样方法对不平衡数据进行预处理来提高分类效果的方法。数据抽样算法有很多,但可以归为两大类：过抽样和欠抽样。针对二分类问题提出了四种融合过抽样和欠抽样算法的重抽样方法：BSM＋Tomek、BSM＋ENN、CBOS＋Tomek和CBOS＋ENN,并且与另外十种经典的重抽样算法做了大量的对比实验,实验证明提出的四种预处理算法在多种评价指标下提高了不平衡数据的分类效果。     

剪枝与欠采样相结合的不平衡数据分类方法*

通过剪枝技术与欠采样技术相结合来选择合适数据,以提高少数类分类精度,研究欠采样技术在不平衡数据集环境下的影响。结果表明,与直接欠采样算法相比,本文算法不仅在accuracy值上有所提高,更重要的是大大改善了g-means值,特别是对非平衡率较大的数据集效果会更好。     

基于改进SMOTE的制造过程不平衡数据分类策略

不平衡数据分析是智能制造的关键技术之一,其分类问题已成为机器学习和数据挖掘的研究热点。针对目前不平衡数据过采样策略中人工合成数据边缘化且需要降噪处理的问题,提出一种基于改进SMOTE（synthetic minority oversampling technique）和局部离群因子（local outlier factor,LOF）的过采样算法。首先对整个数据集进行[K]-means聚类,筛选出高可靠性样本进行改进SMOTE算法过采样,然后采用LOF算法删除误差大的人工合成样本。在4个UCI不平衡数据集上的实验结果表明,该方法对不平衡数据中少数类的分类能力更强,有效地克服了数据边缘化问题,将算法应用于磷酸生产中的不平衡数据,实现了该不平衡数据的准确分类。     

Learning from class-imbalanced data: Review of methods and applications

Rare events, especially those that could potentially negatively impact society, often require humans’ decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.     

RUSBoost: A Hybrid Approach to Alleviating Class Imbalance

Class imbalance is a problem that is common to many application domains. When examples of one class in a training data set vastly outnumber examples of the other class(es), traditional data mining algorithms tend to create suboptimal classification models. Several techniques have been used to alleviate the problem of class imbalance, including data sampling and boosting. In this paper, we present a new hybrid sampling/boosting algorithm, called RUSBoost, for learning from skewed training data. This algorithm provides a simpler and faster alternative to SMOTEBoost, which is another algorithm that combines boosting and data sampling. This paper evaluates the performances of RUSBoost and SMOTEBoost, as well as their individual components (random undersampling, synthetic minority oversampling technique, and AdaBoost). We conduct experiments using 15 data sets from various application domains, four base learners, and four evaluation metrics. RUSBoost and SMOTEBoost both outperform the other procedures, and RUSBoost performs comparably to (and often better than) SMOTEBoost while being a simpler and faster technique. Given these experimental results, we highly recommend RUSBoost as an attractive alternative for improving the classification performance of learners built using imbalanced data.     

面向不平衡数据集的煤矿监测系统异常数据识别方法

异常数据识别对于煤矿安全监测系统具有重要作用,但安全监测系统中异常数据一般只占数据总量的1%左右,不平衡性是此类数据的固有特点。目前多数机器学习算法在不平衡数据集上的分类预测准确率和灵敏度都相对较差。为了能准确识别异常数据,以煤矿分布式光纤竖井变形监测系统采集的数据为研究对象,提出了一种面向不平衡数据集、基于去重复下采样(RDU)、合成少数类过采样技术(SMOTE)和随机森林(RF)分类算法的煤矿监测系统异常数据识别方法。该方法利用RDU算法对多数类数据进行下采样,去除重复样本;利用SMOTE算法对少数类异常数据进行过采样,通过合成新的异常数据来改善数据集的不平衡性;并利用优化后的数据集训练RF分类算法,得到异常数据识别模型。在6个真实数据集上的对比实验结果表明,该方法的异常数据识别准确率平均值达到99.3%,具有较好的泛化性和较强的鲁棒性。     

一种基于混合重取样策略的非均衡数据集分类算法

非均衡数据是分类中的常见问题,当一类实例远远多于另一类实例,则代表类非均衡,真实世界的分类问题存在很多类别非均衡的情况并得到众多专家学者的重视,非均衡数据的分类问题已成为数据挖掘和模式识别领域中新的研究热点,是对传统分类算法的重大挑战。本文提出了一种新型重取样算法,采用改进的SMOTE算法对少数类数据进行过取样,产生新的少数类样本,使类之间数据量基本均衡,然后再根据SMO算法的特点,提出使用聚类的数据欠取样方法,删除冗余或噪音数据。通过对数据集的过取样和清理之后,一些有用的样本被保留下来,减少了数据集规模,增强支持向量机训练执行的效率。实验结果表明,该方法在保持整体分类性能的情况下可以有效地提高少数类的分类精度。