Bagging中文文本分类器的改进方法研究

在文本分类研究中,集成学习是一种提高分类器性能的有效方法.Bagging算法是目前流行的一种集成学习算法.针对Bagging算法弱分类器具有相同权重问题,提出一种改进的Bagging算法.该方法通过对弱分类器分类结果进行可信度计算得到投票权重,应用于Attribute Bagging算法设计了一个中文文本自动分类器.采用kNN作为弱分类器基本模型对Sogou实验室提供的新闻集进行分类.实验表明该算法比Attribute Bagging有更好的分类精度.     

基于正则化互信息和差异度的集成特征选择

如何构造差异性大的基分类器是集成学习研究的重点,为此提出迭代循环选择法:以最大化正则互信息为准则提取最优特征子集,进而基于此训练得到基分类器;同时以错分样本个数作为差异性度量准则来评价所得基分类器的性能,若满足条件则停止,反之则循环迭代直至结束.最后用加权投票法融合所选基分类器的识别结果.通过仿真实验验证算法的有效性,以支持向量机为分类器,在公共数据集UCI上进行实验,并与单SVM及经典的Bagging集成算法和特征Bagging集成算法进行对比.实验结果显示,该方法可获得较高的分类精度.     

基于BHNB的细粒度的Android恶意应用检测模型

为进一步提高Android恶意应用的检测效率,提出一种基于BHNB(Bagging Hierarchical Na?ve Bayesian)的细粒度Android恶意应用检测模型。该模型首先对样本库中的应用进行类别划分,并分别对其进行动态分析,提取各个应用程序的行为信息作为特征;然后,采用层次朴素贝叶斯HNB(Hierarchical Na?ve Bayesian)分类算法对各类应用特征集合进行分别训练,从而构建出多个层次朴素贝叶斯分类器;最后,采用Bagging集成学习方法对构建出的多个层次朴素贝叶斯分类器进行集成学习,构建出基于层次朴素贝叶斯的Bagging集成学习分类器BHNB。实验结果表明,该模型能够有效检测出Android恶意应用,且检测效率较高。     

基于约束投影的支持向量机选择性集成

提出两种基于约束投影的支持向量机选择性集成算法。首先利用随机选取的must-link和cannot-link成对约束集确定投影矩阵,将原始训练样本投影到不同的低维空间训练一组基分类器;然后,分别采用遗传优化和最小化偏离度误差两种选择性集成技术对基分类器进行组合。基于UCI数据的实验表明,提出的两种集成算法均能有效提高支持向量机的泛化性能,显著优于Bagging,Boosting,特征Bagging及LoBag等集成算法。     

基于级联结构的不平衡数据集分类研究

受级联结构的启示,提出了一种针对不平衡数据集分类的新方法,基于级联结构的Bagging分类方法。该方法通过在每一级剔除一部分多数类样本的方式使数据集逐步趋于平衡,并应用欠取样技术得到训练集,用Bagging算法训练分类器,最后把每一级训练到的分类器集成为一个新的分类器。在10个UCI数据集上的实验结果表明,该方法在查全率和F-value值上优于Bagging和AdaBoost。     

Bagging组合的不平衡数据分类方法

提出一种基于Bagging组合的不平衡数据分类方法CombineBagging,采用少数类过抽样算法SMOTE进行数据预处理,在此基础上利用C-SVM、径向基函数神经网络、Random Forests 3种不同的基分类器学习算法,分别对采样后的数据样本进行Bagging集成学习,通过投票规则集成学习结果。实验结果表明,该方法能够提高少数类的分类准确率,有效处理不平衡数据分类问题。     

三种用于垃圾网页检测的随机欠采样集成分类器

针对垃圾网页检测过程中轻微的不平衡分类问题,提出三种随机欠采样集成分类器算法,分别为一次不放回随机欠采样（RUS-once）、多次不放回随机欠采样（RUS-multiple）和有放回随机欠采样（RUS-replacement）算法。首先使用其中一种随机欠采样技术将训练样本集转换成平衡样本集,然后对每个平衡样本集使用分类回归树（CART）分类器算法进行分类,最后采用简单投票法构建集成分类器对测试样本进行分类。实验表明,三种随机欠采样集成分类器均取得了良好的分类效果,其中RUS-multiple和RUS-replacement比RUS-once的分类效果更好。与CART及其Bagging和Adaboost集成分类器相比,在WEBSPAM UK-2006数据集上,RUS-multiple和RUS-replacement方法的AUC指标值提高了10%左右,在WEBSPAM UK-2007数据集上,提高了25%左右;与其他最优研究结果相比,RUS-multiple和RUS-replacement方法在AUC指标上能达到最优分类结果。     

基于分类器性能评价的Bagging文本分类算法

提出一个文本分类器性能评价模型,对文本分类结果的可信度进行了估计,给出计算可信度的公式。将每一个子分类器的可信度指标用于Bagging集成学习算法,得到了改进的基于子分类器性能评价的Bagging算法(PBagging)。应用支持向量机作为子分类器基本模型,对日本共同社大样本新闻集进行分类。实验表明,与Bagging算法相比,PBagging算法分类准确率有了明显提高。     

一种多强度攻击下的对抗逃避攻击集成学习算法

在对抗性学习中,攻击者在非法目的的驱使下,通过探索分类器的漏洞并利用漏洞,使得恶意样本逃过分类器的检测。目前,对抗性学习已被广泛应用于计算机网络中的入侵检测、垃圾邮件过滤和生物识别等领域。现有研究者仅把现有的集成方法应用在对抗性分类中,并证明了多分类器比单分类器更鲁棒。然而,在对抗性学习中,攻击者的先验信息对分类器的鲁棒性有较大的影响。基于此,通过在学习过程中模拟不同强度的攻击,并增大错分样本的权重,提出的 多强度攻击下的对抗逃避攻击集成学习算法 可以在保持多分类器准确性的同时提高鲁棒性。将其与Bagging集成的多分类器进行比较,结果表明所提算法 具有更强的鲁棒性。最后,分析了算法的收敛性以及参数对算法的影响。     

Bagging算法在中文文本分类中的应用

Bagging算法是目前一种流行的集成学习算法,采用一种改进的Bagging算法Attribute Bagging作为分类算法,通过属性重取样获取多个训练集,以kNN为弱分类器设计一种中文文本分类器。实验结果表明Attribute Bagging算法较Bagging算法有更好的分类精度。     

A comparison of decision tree ensemble creation techniques

We experimentally evaluate bagging and seven other randomization-based approaches to creating an ensemble of decision tree classifiers. Statistical tests were performed on experimental results from 57 publicly available data sets. When cross-validation comparisons were tested for statistical significance, the best method was statistically more accurate than bagging on only eight of the 57 data sets. Alternatively, examining the average ranks of the algorithms across the group of data sets, we find that boosting, random forests, and randomized trees are statistically significantly better than bagging. Because our results suggest that using an appropriate ensemble size is important, we introduce an algorithm that decides when a sufficient number of classifiers has been created for an ensemble. Our algorithm uses the out-of-bag error estimate, and is shown to result in an accurate ensemble for those methods that incorporate bagging into the construction of the ensemble     

Out-of-bag estimation of the optimal sample size in bagging

The performance of m-out-of-n bagging with and without replacement in terms of the sampling ratio (m/n) is analyzed. Standard bagging uses resampling with replacement to generate bootstrap samples of equal size as the original training set m_wor=n. Without-replacement methods typically use half samples m_wr=n/2. These choices of sampling sizes are arbitrary and need not be optimal in terms of the classification performance of the ensemble. We propose to use the out-of-bag estimates of the generalization accuracy to select a near-optimal value for the sampling ratio. Ensembles of classifiers trained on independent samples whose size is such that the out-of-bag error of the ensemble is as low as possible generally improve the performance of standard bagging and can be efficiently built.     

Bagging with Adaptive Costs

Ensemble methods have proven to be highly effective in improving the performance of base learners under most circumstances. In this paper, we propose a new algorithm that combines the merits of some existing techniques, namely, bagging, arcing, and stacking. The basic structure of the algorithm resembles bagging. However, the misclassification cost of each training point is repeatedly adjusted according to its observed out-of-bag vote margin. In this way, the method gains the advantage of arcing-building the classifier the ensemble needs - without fixating on potentially noisy points. Computational experiments show that this algorithm performs consistently better than bagging and arcing with linear and nonlinear base classifiers. In view of the characteristics of bacing, a hybrid ensemble learning strategy, which combines bagging and different versions of bacing, is proposed and studied empirically.     

Trimmed bagging

Bagging has been found to be successful in increasing the predictive performance of unstable classifiers. Bagging draws bootstrap samples from the training sample, applies the classifier to each bootstrap sample, and then averages over all obtained classification rules. The idea of trimmed bagging is to exclude the bootstrapped classification rules that yield the highest error rates, as estimated by the out-of-bag error rate, and to aggregate over the remaining ones. In this note we explore the potential benefits of trimmed bagging. On the basis of numerical experiments, we conclude that trimmed bagging performs comparably to standard bagging when applied to unstable classifiers as decision trees, but yields better results when applied to more stable base classifiers, like support vector machines.     

An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation

Several pruning strategies that can be used to reduce the size and increase the accuracy of bagging ensembles are analyzed. These heuristics select subsets of complementary classifiers that, when combined, can perform better than the whole ensemble. The pruning methods investigated are based on modifying the order of aggregation of classifiers in the ensemble. In the original bagging algorithm, the order of aggregation is left unspecified. When this order is random, the generalization error typically decreases as the number of classifiers in the ensemble increases. If an appropriate ordering for the aggregation process is devised, the generalization error reaches a minimum at intermediate numbers of classifiers. This minimum lies below the asymptotic error of bagging. Pruned ensembles are obtained by retaining a fraction of the classifiers in the ordered ensemble. The performance of these pruned ensembles is evaluated in several benchmark classification tasks under different training conditions. The results of this empirical investigation show that ordered aggregation can be used for the efficient generation of pruned ensembles that are competitive, in terms of performance and robustness of classification, with computationally more costly methods that directly select optimal or near-optimal subensembles.     

递减样本集成学习算法

从多个弱分类器重构出强分类器的集成学习方法是机器学习领域的重要研究方向之一。尽管已有多种多样性基本分类器的生成方法被提出,但这些方法的鲁棒性仍有待提高。递减样本集成学习算法综合了目前最为流行的boosting与bagging算法的学习思想,通过不断移除训练集中置信度较高的样本,使训练集空间依次递减,使得某些被低估的样本在后续的分类器中得到充分训练。该策略形成一系列递减的训练子集,因而也生成一系列多样性的基本分类器。类似于boosting与bagging算法,递减样本集成学习方法采用投票策略对基本分类器进行整合。通过严格的十折叠交叉检验,在8个UCI数据集与7种基本分类器上的测试表明,递减样本集成学习算法总体上要优于boosting与bagging算法。     

Building bagging on critical instances

The ensemble method is a powerful data mining paradigm, which builds a classification model by integrating multiple diversified component learners. Bagging is one of the most successful ensemble methods. It is made of bootstrap-inspired classifiers and uses these classifiers to get an aggregated classifier. However, in bagging, bootstrapped training sets become more and more similar as redundancy is increasing. Besides redundancy, any training set is usually subject to noise. Moreover, the training set might be imbalanced. Thus, each training instance has a different impact on the learning process. This paper explores some properties of the ensemble margin and its use in improving the performance of bagging. We introduce a new approach to measure the importance of training data in learning, based on the margin theory. Then, a new bagging method concentrating on critical instances is proposed. This method is more accurate than bagging and more robust than boosting. Compared to bagging, it reduces the bias while generally keeping the same variance. Our findings suggest that (a) examples with low margins tend to be more critical for the classifier performance; (b) examples with higher margins tend to be more redundant; (c) misclassified examples with high margins tend to be noisy examples. Our experimental results on 15 various data sets show that the generalization error of bagging can be reduced up to 2.5% and its resilience to noise strengthened by iteratively removing both typical and noisy training instances, reducing the training set size by up to 75%.     

Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets

We present attribute bagging (AB), a technique for improving the accuracy and stability of classifier ensembles induced using random subsets of features. AB is a wrapper method that can be used with any learning algorithm. It establishes an appropriate attribute subset size and then randomly selects subsets of features, creating projections of the training set on which the ensemble classifiers are built. The induced classifiers are then used for voting. This article compares the performance of our AB method with bagging and other algorithms on a hand-pose recognition dataset. It is shown that AB gives consistently better results than bagging, both in accuracy and stability. The performance of ensemble voting in bagging and the AB method as a function of the attribute subset size and the number of voters for both weighted and unweighted voting is tested and discussed. We also demonstrate that ranking the attribute subsets by their classification accuracy and voting using only the best subsets further improves the resulting performance of the ensemble.     

Multi-armed bandit heterogeneous ensemble learning for imbalanced data

One of the most widely used approaches to the class-imbalanced issue is ensemble learning. The base classifier is trained using an unbalanced training set in the conventional ensemble learning approach. We are unable to select the best suitable resampling method or base classifier for the training set, despite the fact that researchers have examined employing resampling strategies to balance the training set. A multi-armed bandit heterogeneous ensemble framework was developed as a solution to these issues. This framework employs the multi-armed bandit technique to pick the best base classifier and resampling techniques to build a heterogeneous ensemble model. To obtain training sets, we first employ the bagging technique. Then, we use the instances from the out-of-bag set as the validation set. In general, we consider the basic classifier combination with the highest validation set score to be the best model on the bagging subset and add it to the pool of model. The classification performance of the multi-armed bandit heterogeneous ensemble model is then assessed using 30 real-world imbalanced data sets that were gathered from UCI, KEEL, and HDDT. The experimental results demonstrate that, under the two assessment metrics of AUC and Kappa, the proposed heterogeneous ensemble model performs competitively with other nine state-of-the-art ensemble learning methods. At the same time, the findings of the experiment are confirmed by the statistical findings of the Friedman test and Holm's post-hoc test.     

A feature selection enabled hybrid‐bagging algorithm for credit risk evaluation

Hybrid models based on feature selection and machine learning techniques have significantly enhanced the accuracy of standalone models. This paper presents a feature selection‐based hybrid‐bagging algorithm (FS‐HB) for improved credit risk evaluation. The 2 feature selection methods chi‐square and principal component analysis were used for ranking and selecting the important features from the datasets. The classifiers were built on 5 training and test data partitions of the input data set. The performance of the hybrid algorithm was compared with that of the standalone classifiers: feature selection‐based classifiers and bagging. The hybrid FS‐HB algorithm performed best for qualitative dataset with less features and tree‐based unstable base classifier. Its performance on numeric data was also better than other standalone classifiers, whereas comparable to bagging with only selected features. Its performance was found better on 70:30 data partition and the type II error, which is very significant in risk evaluation was also reduced significantly. The improved performance of FS‐HB is attributed to the important features used for developing the classifier thereby reducing the complexity of the algorithm and the use of ensemble methodology, which added to the classical bias variance trade‐off and performed better than standalone classifiers.