Improved classification with allocation method and multiple classifiers

Classification is the most used supervized machine learning method. As each of the many existing classification algorithms can perform poorly on some data, different attempts have arisen to improve the original algorithms by combining them. Some of the best know results are produced by ensemble methods, like bagging or boosting. We developed a new ensemble method called allocation. Allocation method uses the allocator, an algorithm that separates the data instances based on anomaly detection and allocates them to one of the micro classifiers, built with the existing classification algorithms on a subset of training data. The outputs of micro classifiers are then fused together into one final classification. Our goal was to improve the results of original classifiers with this new allocation method and to compare the classification results with existing ensemble methods. The allocation method was tested on 30 benchmark datasets and was used with six well known basic classification algorithms (J48, NaiveBayes, IBk, SMO, OneR and NBTree). The obtained results were compared to those of the basic classifiers as well as other ensemble methods (bagging, MultiBoost and AdaBoost). Results show that our allocation method is superior to basic classifiers and also to tested ensembles in classification accuracy and f-score. The conducted statistical analysis, when all of the used classification algorithms are considered, confirmed that our allocation method performs significantly better both in classification accuracy and f-score. Although the differences are not significant for each of the used basic classifier alone, the allocation method achieved the biggest improvements on all six basic classification algorithms. In this manner, allocation method proved to be a competitive ensemble method for classification that can be used with various classification algorithms and can possibly outperform other ensembles on different types of data.     

Applying Ant Colony Optimization to configuring stacking ensembles for data mining

An ensemble is a collective decision-making system which applies a strategy to combine the predictions of learned classifiers to generate its prediction of new instances. Early research has proved that ensemble classifiers in most cases can be more accurate than any single component classifier both empirically and theoretically. Though many ensemble approaches are proposed, it is still not an easy task to find a suitable ensemble configuration for a specific dataset. In some early works, the ensemble is selected manually according to the experience of the specialists. Metaheuristic methods can be alternative solutions to find configurations. Ant Colony Optimization (ACO) is one popular approach among metaheuristics. In this work, we propose a new ensemble construction method which applies ACO to the stacking ensemble construction process to generate domain-specific configurations. A number of experiments are performed to compare the proposed approach with some well-known ensemble methods on 18 benchmark data mining datasets. The approach is also applied to learning ensembles for a real-world cost-sensitive data mining problem. The experiment results show that the new approach can generate better stacking ensembles.     

Combining Classifiers with Meta Decision Trees

The paper introduces meta decision trees (MDTs), a novel method for combining multiple classifiers. Instead of giving a prediction, MDT leaves specify which classifier should be used to obtain a prediction. We present an algorithm for learning MDTs based on the C4.5 algorithm for learning ordinary decision trees (ODTs). An extensive experimental evaluation of the new algorithm is performed on twenty-one data sets, combining classifiers generated by five learning algorithms: two algorithms for learning decision trees, a rule learning algorithm, a nearest neighbor algorithm and a naive Bayes algorithm. In terms of performance, stacking with MDTs combines classifiers better than voting and stacking with ODTs. In addition, the MDTs are much more concise than the ODTs and are thus a step towards comprehensible combination of multiple classifiers. MDTs also perform better than several other approaches to stacking.     

A comparative assessment of ensemble learning for credit scoring

Both statistical techniques and Artificial Intelligence (AI) techniques have been explored for credit scoring, an important finance activity. Although there are no consistent conclusions on which ones are better, recent studies suggest combining multiple classifiers, i.e., ensemble learning, may have a better performance. In this study, we conduct a comparative assessment of the performance of three popular ensemble methods, i.e., Bagging, Boosting, and Stacking, based on four base learners, i.e., Logistic Regression Analysis (LRA), Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). Experimental results reveal that the three ensemble methods can substantially improve individual base learners. In particular, Bagging performs better than Boosting across all credit datasets. Stacking and Bagging DT in our experiments, get the best performance in terms of average accuracy, type I error and type II error.     

软件缺陷集成预测模型研究

利用单一分类器构造的缺陷预测模型已经遇到了性能瓶颈, 而集成分类器相比单一分类器往往具有显著的性能优势。以构造高效的集成缺陷预测模型为出发点, 比较了七种不同类型集成分类器的算法和特点。在14个基准数据集上的实验显示, 部分集成预测模型的性能优于基于朴素贝叶斯的单一预测模型。其中, 基于投票的集成分类框架具有最优的预测性能以及统计学意义上的性能优势显著性, 随机森林算法次之。Stacking集成框架也具有较强的泛化能力。     

基于堆叠集成的数据流分类*

对数据流分类分析的常用方法是集成学习。为了得到更好的分类效果,给出一种基于堆叠集成的数据流分类分析方法。该方法通过构造一个分类器对基分类器进行集成。实验结果表明,与基于投票或加权投票的集成方法相比,基于堆叠集成方法对概念漂移的快速适应能力以及预测准确率得到了提高。     

基于多特征和Stacking算法的Android恶意软件检测方法

Android由于其广泛的普及率使得其平台上的恶意软件数量不断增加,针对目前大部分方法采用单一特征和单一算法进行检验,准确率不高的不足,提出了一种基于多特征与Stacking算法的静态检测方法,该方法能够弥补这两方面的不足. 首先使用多种特征信息组成特征向量,并且使用Stacking集成学习算法组合Logistic,SVM,k近邻和CART决策树多个基本算法,再通过训练样本进行学习形成分类器. 实验结果表明,相对于使用单一特征和单一算法其识别准确率得到提高,可达94.05%,该分类器对测试样本拥有较好的识别性能.     

CSBF: A static ensemble fusion method based on the centrality score of complex networks

Ensemble of classifiers can improve classification accuracy by combining several models. The fusion method plays an important role in the ensemble performance. Usually, a criterion for weighting the decision of each ensemble member is adopted. Frequently, this can be done using some heuristic based on accuracy or confidence. Then, the used fusion rule must consider the established criterion for providing a most reliable ensemble output through a kind of competition among the ensemble members. This article presents a new ensemble fusion method, named centrality score-based fusion, which uses the centrality concept in the context of social network analysis (SNA) as a criterion for the ensemble decision. Centrality measures have been applied in the SNA to measure the importance of each person inside of a social network, taking into account the relationship of each person with all others. Thus, the idea is to derive the classifier weight considering the overall classifier prominence inside the ensemble network, which reflects the relationships among pairs of classifiers. We hypothesized that the prominent position of a classifier based on its pairwise relationship with the other ensemble members could be its weight in the fusion process. A robust experimental protocol has confirmed that centrality measures represent a promising strategy to weight the classifiers of an ensemble, showing that the proposed fusion method performed well against the literature.     

A variant of Rotation Forest for constructing ensemble classifiers

Rotation Forest, an effective ensemble classifier generation technique, works by using principal component analysis (PCA) to rotate the original feature axes so that different training sets for learning base classifiers can be formed. This paper presents a variant of Rotation Forest, which can be viewed as a combination of Bagging and Rotation Forest. Bagging is used here to inject more randomness into Rotation Forest in order to increase the diversity among the ensemble membership. The experiments conducted with 33 benchmark classification data sets available from the UCI repository, among which a classification tree is adopted as the base learning algorithm, demonstrate that the proposed method generally produces ensemble classifiers with lower error than Bagging, AdaBoost and Rotation Forest. The bias–variance analysis of error performance shows that the proposed method improves the prediction error of a single classifier by reducing much more variance term than the other considered ensemble procedures. Furthermore, the results computed on the data sets with artificial classification noise indicate that the new method is more robust to noise and kappa-error diagrams are employed to investigate the diversity–accuracy patterns of the ensemble classifiers.     

基于精简随机子空间的多生成树集成一类分类算法

由于高维数据通常存在冗余和噪声,在其上直接构造覆盖模型不能充分反映数据的分布信息,导致分类器性能下降.为此提出一种基于精简随机子空间多树集成分类方法.该方法首先生成多个随机子空间,并在每个子空间上构造独立的最小生成树覆盖模型.其次对每个子空间上构造的分类模型进行精简处理,通过一个评估准则(AUC值),对生成的一类分类器进行精简.最后均值合并融合这些分类器为一个集成分类器.实验结果表明,与其它直接覆盖分类模型和bagging算法相比,多树集成覆盖分类器具有更高的分类正确率.     

Building ensemble classifiers using belief functions and OWA operators

A pervasive task in many forms of human activity is classification. Recent interest in the classification process has focused on ensemble classifier systems. These types of systems are based on a paradigm of combining the outputs of a number of individual classifiers. In this paper we propose a new approach for obtaining the final output of ensemble classifiers. The method presented here uses the Dempster–Shafer concept of belief functions to represent the confidence in the outputs of the individual classifiers. The combing of the outputs of the individual classifiers is based on an aggregation process which can be seen as a fusion of the Dempster rule of combination with a generalized form of OWA operator. The use of the OWA operator provides an added degree of flexibility in expressing the way the aggregation of the individual classifiers is performed.     

A new hybrid ensemble credit scoring model based on classifiers consensus system approach

During the last few years there has been marked attention towards hybrid and ensemble systems development, having proved their ability to be more accurate than single classifier models. However, among the hybrid and ensemble models developed in the literature there has been little consideration given to: 1) combining data filtering and feature selection methods 2) combining classifiers of different algorithms; and 3) exploring different classifier output combination techniques other than the traditional ones found in the literature. In this paper, the aim is to improve predictive performance by presenting a new hybrid ensemble credit scoring model through the combination of two data pre-processing methods based on Gabriel Neighbourhood Graph editing (GNG) and Multivariate Adaptive Regression Splines (MARS) in the hybrid modelling phase. In addition, a new classifier combination rule based on the consensus approach (ConsA) of different classification algorithms during the ensemble modelling phase is proposed. Several comparisons will be carried out in this paper, as follows: 1) Comparison of individual base classifiers with the GNG and MARS methods applied separately and combined in order to choose the best results for the ensemble modelling phase; 2) Comparison of the proposed approach with all the base classifiers and ensemble classifiers with the traditional combination methods; and 3) Comparison of the proposed approach with recent related studies in the literature. Five of the well-known base classifiers are used, namely, neural networks (NN), support vector machines (SVM), random forests (RF), decision trees (DT), and naïve Bayes (NB). The experimental results, analysis and statistical tests prove the ability of the proposed approach to improve prediction performance against all the base classifiers, hybrid and the traditional combination methods in terms of average accuracy, the area under the curve (AUC) H-measure and the Brier Score. The model was validated over seven real world credit datasets.     

一种新的基于多样性赋权证据推理的集成学习方法

在集成学习中使用平均法、投票法作为结合策略无法充分利用基分类器的有效信息,且根据波动性设置基分类器的权重不精确、不恰当。以上问题会降低集成学习的效果,为了进一步提高集成学习的性能,提出将证据推理(evidence reasoning, ER)规则作为结合策略,并使用多样性赋权法设置基分类器的权重。首先,由多个深度学习模型作为基分类器、ER规则作为结合策略,构建集成学习的基本结构;然后,通过多样性度量方法计算每个基分类器相对于其他基分类器的差异性;最后,将差异性归一化实现基分类器的权重设置。通过多个图像数据集的分类实验,结果表明提出的方法较实验选取的其他方法准确率更高且更稳定,证明了该方法可以充分利用基分类器的有效信息,且多样性赋权法更精确。     

Is Combining Classifiers with Stacking Better than Selecting the Best One?

We empirically evaluate several state-of-the-art methods for constructing ensembles of heterogeneous classifiers with stacking and show that they perform (at best) comparably to selecting the best classifier from the ensemble by cross validation. Among state-of-the-art stacking methods, stacking with probability distributions and multi-response linear regression performs best. We propose two extensions of this method, one using an extended set of meta-level features and the other using multi-response model trees to learn at the meta-level. We show that the latter extension performs better than existing stacking approaches and better than selecting the best classifier by cross validation.     

Classification of major construction materials in construction environments using ensemble classifiers

The automatic detection of construction materials in images acquired on a construction site has been regarded as a critical topic. Recently, several data mining techniques have been used as a way to solve the problem of detecting construction materials. These studies have applied single classifiers to detect construction materials—and distinguish them from the background—by using color as a feature. Recent studies suggest that combining multiple classifiers (into what is called a heterogeneous ensemble classifier) would show better performance than using a single classifier. However, the performance of ensemble classifiers in construction material detection is not fully understood. In this study, we investigated the performance of six single classifiers and potential ensemble classifiers on three data sets: one each for concrete, steel, and wood. A heterogeneous voting-based ensemble classifier was created by selecting base classifiers which are diverse and accurate; their prediction probabilities for each target class were averaged to yield a final decision for that class. In comparison with the single classifiers, the ensemble classifiers performed better in the three data sets overall. This suggests that it is better to use an ensemble classifier to enhance the detection of construction materials in images acquired on a construction site.     

一种基于多特征集成学习的恶意代码静态检测框架

伴随着互联网的普及和5G通信技术的快速发展,网络空间所面临的威胁日益增大,尤其是恶意软件的数量呈指数型上升,其所属家族的变种爆发式增加.传统的基于人工签名的恶意软件的检测方式速度太慢,难以处理每天数百万计新增的恶意软件,而普通的机器学习分类器的误报率和漏检率又明显过高.同时恶意软件的加壳、混淆等对抗技术对该情况造成了更大的困扰.基于此,提出一种基于多特征集成学习的恶意软件静态检测框架.通过提取恶意软件的非PE(Portable Executable)结构特征、可见字符串与汇编码序列特征、PE结构特征以及函数调用关系5部分特征,构建与各部分特征相匹配的模型,采用Bagging集成和Stacking集成算法,提升模型的稳定性,降低过拟合的风险.然后采取权重策略投票算法对5部分集成模型的输出结果做进一步聚合.经过测试,多特征多模型聚合的检测准确率可达96.99%,该结果表明:与其他静态检测方法相比,该方法具有更好的恶意软件鉴别能力,对加壳、混淆等恶意软件同样具备较高的识别率.     

适于数据流组合分类的直推学习方法

在进行组合决策时,已有的组合分类方法需要对多个组合分类器均有效的公共已知标签训练样本。为了解决在没有已知标签样本的情况下数据流组合分类决策问题,提出一种基于约束学习的数据流组合分类器的融合策略。在判定测试样本上的决策时,根据直推学习理论设计满足每一个局部分类器约束度量的方法,保证了约束的可行性,解决了分布式分类聚集时最大熵的直推扩展问题。测试数据集上的实验证明,与已有的直推学习方法相比,此方法可以获得更好的决策精度,可以应用于数据流组合分类的融合。     

Statistical approaches to combining binary classifiers for multi-class classification

One of the popular methods for multi-class classification is to combine binary classifiers. In this paper, we propose a new approach for combining binary classifiers. Our method trains a combining method of binary classifiers using statistical techniques such as penalized logistic regression, stacking, and a sparsity promoting penalty. Our approach has several advantages. Firstly, our method outperforms existing methods even if the base classifiers are well-tuned. Secondly, an estimate of conditional probability for each class can be naturally obtained. Furthermore, we propose selecting relevant binary classifiers by adding the group lasso type penalty in training the combining method.     

Greedy optimization classifiers ensemble based on diversity

Decreasing the individual error and increasing the diversity among classifiers are two crucial factors for improving ensemble performances. Nevertheless, the “kappa-error” diagram shows that enhancing the diversity is at the expense of reducing individual accuracy. Hence, a new method named Matching Pursuit Optimization Ensemble Classifiers (MPOEC) is proposed in this paper in order to balance the diversity and the individual accuracy. MPOEC method adopts a greedy iterative algorithm of matching pursuit to search for an optimal combination of entire classifiers, and eliminates some similar or poor classifiers by giving zero coefficients. In MPOEC approach, the coefficient of every classifier is gained by minimizing the residual between the target function and the linear combination of the basis functions, especially, when the basis functions are similar, their coefficients will be close to zeros in one iteration of the optimization process, which indicates that obtained coefficients of classifiers are based on the diversity among ensemble individuals. Because some classifiers are given zero coefficients, MPOEC approach may be also considered as a selective classifiers ensemble method. Experimental results show that MPOEC improves the performance compared with other methods. Furthermore, the kappa-error diagrams indicate that the diversity is increased by the proposed method compared with standard ensemble strategies and evolutionary ensemble.     

A genetic encoding approach for learning methods for combining classifiers

Several studies have reported that the ensemble of classifiers can improve the performance of a stand-alone classifier. In this paper, we propose a learning method for combining the predictions of a set of classifiers.The method described in this paper uses a genetic-based version of the correspondence analysis for combining classifiers. The correspondence analysis is based on the orthonormal representation of the labels assigned to the patterns by a pool of classifiers. In this paper instead of the orthonormal representation we use a pool of representations obtained by a genetic algorithm. Each single representation is used to train a different classifiers, these classifiers are combined by vote rule.The performance improvement with respect to other learning-based fusion methods is validated through experiments with several benchmark datasets.