一种适用于多类不平衡数据集的模糊关联分类方法

提出一种适用于多类不平衡分布情形下的模糊关联分类方法,该方法以最小化AdaBoost.M1W集成学习迭代过程中训练样本的加权分类错误率和子分类器中模糊关联分类规则数目及规则中所含模糊项的数目为遗传优化目标,实现了AdaBoost.M1W和模糊关联分类建模过程的较好融合.通过5个多类不平衡UCI标准数据集和现有的针对不平衡分类问题的数据预处理方法实验对比结果,表明了所提出的方法能显著提高多类不平衡情形下的模糊关联分类模型的分类性能.     

基于GAN-AdaBoost-DT不平衡分类算法的信用卡欺诈分类

针对传统单个分类器在不平衡数据上分类效果有限的问题，基于对抗生成网络（GAN）和集成学习方法，提出一种新的针对二类不平衡数据集的分类方法——对抗生成网络-自适应增强-决策树（GAN-AdaBoost-DT）算法。首先，利用GAN训练得到生成模型，生成模型生成少数类样本，降低数据的不平衡性；其次，将生成的少数类样本代入自适应增强（AdaBoost）模型框架，更改权重，改进AdaBoost模型，提升以决策树（DT）为基分类器的AdaBoost模型的分类性能。使用受测者工作特征曲线下面积（AUC）作为分类评价指标，在信用卡诈骗数据集上的实验分析表明，该算法与合成少数类样本集成学习相比，准确率提高了4.5%，受测者工作特征曲线下面积提高了6.5%；对比改进的合成少数类样本集成学习，准确率提高了4.9%，AUC值提高了5.9%；对比随机欠采样集成学习，准确率提高了4.5%，受测者工作特征曲线下面积提高了5.4%。在UCI和KEEL的其他数据集上的实验结果表明，该算法在不平衡二分类问题上能提高总体的准确率，优化分类器性能。     

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.     

Adaptive ensemble of classifiers with regularization for imbalanced data classification

The dynamic ensemble selection of classifiers is an effective approach for processing label-imbalanced data classifications. However, such a technique is prone to overfitting, owing to the lack of regularization methods and the dependence on local geometry of data. In this study, focusing on binary imbalanced data classification, a novel dynamic ensemble method, namely adaptive ensemble of classifiers with regularization (AER), is proposed, to overcome the stated limitations. The method solves the overfitting problem through a new perspective of implicit regularization. Specifically, it leverages the properties of stochastic gradient descent to obtain the solution with the minimum norm, thereby achieving regularization; furthermore, it interpolates the ensemble weights by exploiting the global geometry of data to further prevent overfitting. According to our theoretical proofs, the seemingly complicated AER paradigm, in addition to its regularization capabilities, can actually reduce the asymptotic time and memory complexities of several other algorithms. We evaluate the proposed AER method on seven benchmark imbalanced datasets from the UCI machine learning repository and one artificially generated GMM-based dataset with five variations. The results show that the proposed algorithm outperforms the major existing algorithms based on multiple metrics in most cases, and two hypothesis tests (McNemar’s and Wilcoxon tests) verify the statistical significance further. In addition, the proposed method has other preferred properties such as special advantages in dealing with highly imbalanced data, and it pioneers the researches on regularization for dynamic ensemble methods.     

Multi-window based ensemble learning for classification of imbalanced streaming data

Imbalanced streaming data is commonly encountered in real-world data mining and machine learning applications, and has attracted much attention in recent years. Both imbalanced data and streaming data in practice are normally encountered together; however, little research work has been studied on the two types of data together. In this paper, we propose a multi-window based ensemble learning method for the classification of imbalanced streaming data. Three types of windows are defined to store the current batch of instances, the latest minority instances, and the ensemble classifier. The ensemble classifier consists of a set of latest sub-classifiers, and the instances employed to train each sub-classifier. All sub-classifiers are weighted prior to predicting the class labels of newly arriving instances, and new sub-classifiers are trained only when the precision is below a predefined threshold. Extensive experiments on synthetic datasets and real-world datasets demonstrate that the new approach can efficiently and effectively classify imbalanced streaming data, and generally outperforms existing approaches.     

基于遗传规划集成学习的网络作弊检测

网络作弊检测是搜索引擎的重要挑战之一,该文提出基于遗传规划的集成学习方法 (简记为GPENL)来检测网络作弊。该方法首先通过欠抽样技术从原训练集中抽样得到t个不同的训练集;然后使用c个不同的分类算法对t个训练集进行训练得到t*c个基分类器;最后利用遗传规划得到t*c个基分类器的集成方式。新方法不仅将欠抽样技术和集成学习融合起来提高非平衡数据集的分类性能,还能方便地集成不同类型的基分类器。在WEBSPAM-UK2006数据集上所做的实验表明无论是同态集成还是异态集成,GPENL均能提高分类的性能,且异态集成比同态集成更加有效;GPENL比AdaBoost、Bagging、RandomForest、多数投票集成、EDKC算法和基于Prediction Spamicity的方法取得更高的F-度量值。     

面向不平衡数据集的机器学习分类策略

由于不平衡数据集的内在固有特性,使得分类结果常受数量较多的类别影响,造成分类性能下降。近年来,为了能够从类别不平衡的数据集中学习数据的内在规律并且挖掘其潜在的价值,提出了一系列基于提升不平衡数据集机器学习分类算法准确率的研究策略。这些策略主要是立足于数据层面、分类模型改进层面来解决不平衡数据集分类难的困扰。从以上两个方面论述面向不平衡数据集分类问题的机器学习分类策略,分析和讨论了针对不平衡数据集机器学习分类器的评价指标,总结了不平衡数据集分类尚存在的问题,展望了未来能够深入研究的方向。特别的,这些讨论的研究主要关注类别极端不平衡场景下的二分类问题所面临的困难。     

Similarity-based attribute weighting methods via clustering algorithms in the classification of imbalanced medical datasets

In the fields of pattern recognition and machine learning, the use of data preprocessing algorithms has been increasing in recent years to achieve high classification performance. In particular, it has become inevitable to use the data preprocessing method prior to classification algorithms in classifying medical datasets with the nonlinear and imbalanced data distribution. In this study, a new data preprocessing method has been proposed for the classification of Parkinson, hepatitis, Pima Indians, single proton emission computed tomography (SPECT) heart, and thoracic surgery medical datasets with the nonlinear and imbalanced data distribution. These datasets were taken from UCI machine learning repository. The proposed data preprocessing method consists of three steps. In the first step, the cluster centers of each attribute were calculated using k-means, fuzzy c-means, and mean shift clustering algorithms in medical datasets including Parkinson, hepatitis, Pima Indians, SPECT heart, and thoracic surgery medical datasets. In the second step, the absolute differences between the data in each attribute and the cluster centers are calculated, and then, the average of these differences is calculated for each attribute. In the final step, the weighting coefficients are calculated by dividing the mean value of the difference to the cluster centers, and then, weighting is performed by multiplying the obtained weight coefficients by the attribute values in the dataset. Three different attribute weighting methods have been proposed: (1) similarity-based attribute weighting in k-means clustering, (2) similarity-based attribute weighting in fuzzy c-means clustering, and (3) similarity-based attribute weighting in mean shift clustering. In this paper, we aimed to aggregate the data in each class together with the proposed attribute weighting methods and to reduce the variance value within the class. Thus, by reducing the value of variance in each class, we have put together the data in each class and at the same time, we have further increased the discrimination between the classes. To compare with other methods in the literature, the random subsampling has been used to handle the imbalanced dataset classification. After attribute weighting process, four classification algorithms including linear discriminant analysis, k-nearest neighbor classifier, support vector machine, and random forest classifier have been used to classify imbalanced medical datasets. To evaluate the performance of the proposed models, the classification accuracy, precision, recall, area under the ROC curve, κ value, and F-measure have been used. In the training and testing of the classifier models, three different methods including the 50–50% train–test holdout, the 60–40% train–test holdout, and tenfold cross-validation have been used. The experimental results have shown that the proposed attribute weighting methods have obtained higher classification performance than random subsampling method in the handling of classifying of the imbalanced medical datasets.

     

基于互信息的选择性集成核极端学习机

针对集成学习中的准确性和差异性平衡问题, 提出一种基于信息论的选择性集成核极端学习机. 采用具有结构简单、训练简便、泛化性能好的核极端学习作为基学习器. 引入相关性准则描述准确性, 冗余性准则描述差异性,将选择性集成问题转化为变量选择问题. 利用基于互信息的最大相关最小冗余准则对生成的核极端学习机进行选择, 从而实现准确性和差异性的平衡. 基于UCI 基准回归和分类数据的仿真结果验证了所提出算法的优越性.

     

Dynamic weighted selective ensemble learning algorithm for imbalanced data streams

Data stream mining is one of the hot topics in data mining. Most existing algorithms assume that data stream with concept drift is balanced. However, in real-world, the data streams are imbalanced with concept drift. The learning algorithm will be more complex for the imbalanced data stream with concept drift. In online learning algorithm, the oversampling method is used to select a small number of samples from the previous data block through a certain strategy and add them into the current data block to amplify the current minority class. However, in this method, the number of stored samples, the method of oversampling and the weight calculation of base-classifier all affect the classification performance of ensemble classifier. This paper proposes a dynamic weighted selective ensemble (DWSE) learning algorithm for imbalanced data stream with concept drift. On the one hand, through resampling the minority samples in previous data block, the minority samples of the current data block can be amplified, and the information in the previous data block can be absorbed into building a classifier to reduce the impact of concept drift. The calculation method of information content of every sample is defined, and the resampling method and updating method of the minority samples are given in this paper. On the other hand, because of concept drift, the performance of the base-classifier will be degraded, and the decay factor is usually used to describe the performance degradation of base-classifier. However, the static decay factor cannot accurately describe the performance degradation of the base-classifier with the concept drift. The calculation method of dynamic decay factor of the base-classifier is defined in DWSE algorithm to select sub-classifiers to eliminate according to the attenuation situation, which makes the algorithm better deal with concept drift. Compared with other algorithms, the results show that the DWSE algorithm has better classification performance for majority class samples and minority samples.

     

结合样本局部密度的非平衡数据集成分类算法

传统的过采样方法是解决非平衡数据分类问题的有效方法之一。基于SMOTE的过采样方法在数据集出现类别重叠(class-overlapping)和小析取项(small-disjuncts)问题时将降低采样的效果,针对该问题提出了一种基于样本局部密度的过采样算法MOLAD。在此基础上,为了解决非平衡数据的分类问题,提出了一种在采样阶段将MOLAD算法和基于Bagging的集成学习结合的算法LADBMOTE。LADBMOTE首先根据MOLAD计算每个少数类样本的K近邻,然后选择所有的K近邻进行采样,生成K个平衡数据集,最后利用基于Bagging的集成学习方法将K个平衡数据集训练得到的分类器集成。在KEEL公开的20个非平衡数据集上,将提出的LADBMOTE算法与当前流行的7个处理非平衡数据的算法对比,实验结果表明LADBMOTE在不同的分类器上的分类性能更好,鲁棒性更强。     

DyS-IENN: a novel multiclass imbalanced learning method for early warning of tardiness in rocket final assembly process

Establishing an effective early warning mechanism for the rocket final assembly process (RFAP) is crucial for the timely delivery of rockets and the reduction of additional production costs. To solve the unsystematic design of warning indicators and warning levels in RFAP and address the problem of low warning accuracy caused by imbalanced data distribution, this paper redesigns the warning indicators and warning levels in a systematic way, and develops a novel multiclass imbalanced learning method based on dynamic sampling algorithm (DyS) and improved ensemble neural network (IENN). The DyS algorithm dynamically determines the training set after oversampling the minority class, while the IENN can effectively suppress the oscillation in the iterative process of the DyS algorithm and improve the overall classification accuracy by removing the redundant and ineffective networks from the ensemble neural network. The experiment results indicate that the proposed method outperforms other methods in terms of accuracy and stability for early warning of tardiness in RFAP.

     

基于概率分布估计的混合采样算法

在类别不均衡的数据中, 类间和类内不均衡性问题都是导致分类性能下降的重要因素. 为了提高不均衡数据集下分类算法的性能, 提出一种基于概率分布估计的混合采样算法. 该算法依据数据概率分别对每个子类进行采样以保证类内的均衡性; 并扩大少数类的潜在决策域和减少多数类的冗余信息, 从而同时从全局和局部两个角度改善数据的平衡性. 实验结果表明, 该算法提高了传统分类算法在不均衡数据下的分类性能.

     

基于主动学习SMOTE的非均衡数据分类

少数类样本合成过采样技术(SMOTE)是一种典型的过采样数据预处理方法,它能够有效平衡非均衡数据,但会带来噪音等问题,影响分类精度。为解决此问题,借助主动学习支持向量机的分类性能,提出一种基于主动学习SMOTE的非均衡数据分类方法 ALSMOTE。由于主动学习支持向量机采用基于距离的主动选择最佳样本的学习策略,因此能够主动选择非均衡数据中的有价值的多数类样本,舍弃价值较小的样本,从而提高运算效率,改进SMOTE带来的问题。首先运用SMOTE方法均衡小部分样本,得到初始分类器;然后利用主动学习策略调整分类器精度。实验结果表明,该方法有效提高了非均衡数据的分类准确率。     

Constructing support vector machine ensemble with segmentation for imbalanced datasets

A novel method, namely ensemble support vector machine with segmentation (SeEn–SVM), for the classification of imbalanced datasets is proposed in this paper. In particular, vector quantization algorithm is used to segment the majority class and hence generates some small datasets that are of less imbalance than original one, and two different weighted functions are proposed to integrate all the results of basic classifiers. The goal of the SeEn–SVM algorithm is to improve the prediction accuracy of the minority class, which is more interesting for people. The SeEn–SVM is applied to six UCI datasets, and the results confirmed its better performance than previously proposed methods for imbalance problem.

     

优化的支持向量机集成分类器在非平衡数据集分类中的应用

传统的分类算法大都建立在平衡数据集的基础上,当样本数据不平衡时,这些学习算法的性能往往会明显下降.对于非平衡数据分类问题,提出了一种优化的支持向量机(SVM)集成分类器模型,采用KSMOTE和Bootstrap对非平衡数据进行预处理,生成相应的SVM模型并用复合形算法优化模型参数,最后利用优化的参数并行生成SVM集成分类器模型,采用投票机制得到分类结果.对5组UCI标准数据集进行实验,结果表明采用优化的SVM集成分类器模型较SVM模型、优化的SVM模型等分类精度有了明显的提升,同时验证了不同的bootNum取值对分类器性能效果的影响.     

不平衡数据多粒度集成分类算法研究

针对传统模型在解决不平衡数据分类问题时存在精度低、稳定性差、泛化能力弱等问题,提出基于序贯三支决策多粒度集成分类算法MGE-S3WD。采用二元关系实现粒层动态划分;根据代价矩阵计算阈值并构建多层次粒结构,将各粒层数据划分为正域、边界域和负域;将各粒层上的划分,按照正域与负域、正域与边界域、负域与边界域重新组合形成新的数据子集,并在各数据子集上构建基分类器,实现不平衡数据的集成分类。仿真结果表明,该算法能够有效降低数据子集的不平衡比,提升集成学习中基分类器的差异性,在G-mean和F-measure1 2个评价指标下,分类性能优于或部分优于其他集成分类算法,有效提高了分类模型的分类精度和稳定性,为不平衡数据集的集成学习提供了新的研究思路。     

Class imbalance learning via a fuzzy total margin based support vector machine

The classification of imbalanced data is a major challenge for machine learning. In this paper, we presented a fuzzy total margin based support vector machine (FTM-SVM) method to handle the class imbalance learning (CIL) problem in the presence of outliers and noise. The proposed method incorporates total margin algorithm, different cost functions and the proper approach of fuzzification of the penalty into FTM-SVM and formulates them in nonlinear case. We considered an excellent type of fuzzy membership functions to assign fuzzy membership values and got six FTM-SVM settings. We evaluated the proposed FTM-SVM method on two artificial data sets and 16 real-world imbalanced data sets. Experimental results show that the proposed FTM-SVM method has higher G_Mean and F_Measure values than some existing CIL methods. Based on the overall results, we can conclude that the proposed FTM-SVM method is effective for CIL problem, especially in the presence of outliers and noise in data sets.     

基于差分进化的ELM加权集成分类

集成分类通过将若干个弱分类器依据某种规则进行组合,能有效改善分类性能。在组合过程中,各个弱分类器对分类结果的重要程度往往不一样。极限学习机是最近提出的一个新的训练单隐层前馈神经网络的学习算法。以极限学习机为基分类器,提出了一个基于差分进化的极限学习机加权集成方法。提出的方法通过差分进化算法来优化集成方法中各个基分类器的权值。实验结果表明,该方法与基于简单投票集成方法和基于Adaboost集成方法相比,具有较高的分类准确性和较好的泛化能力。     

一种新的不平衡数据学习算法PCBoost

现实世界中广泛存在不平衡数据,其分类问题是机器学习研究中的一个热点.多数传统分类算法假定类分布平衡或误分类代价均衡,在处理不平衡数据时,效果不够理想.文中提出一种不平衡数据分类算法-PCBoost.算法以信息增益率为分裂准则构建决策树,作为弱分类器.在每次迭代初始,利用数据合成方法添加合成的少数类样例,平衡训练信息;在子分类器形成后,修正“扰动”,删除未被正确分类的合成样例.文中讨论了数据合成方法,给出了训练误差界的理论分析,并分析了集成学习参数的选择.实验结果表明,PCBoost算法具有处理不平衡数据分类问题的优势.