基于多步的多分类器在入侵检测中的研究

海量的数据中总是混杂着多种类型的数据,因此对数据进行处理分类时使用单一的分类器很难进行准确的分类。针对多种类型数据,提出一种基于多步分类的多种数据分类器的入侵检测方法。建立多分类型模型,改进特征选择方法及Bagging;对数据中的多种类型分析时,采用针对某种类型分类效果最佳的分类器,多次完成数据的分类操作。经KDD CUP99数据集实验,结果表明该方法对多数据分类具有显著效果。     

一种基于信息熵的多分类器动态组合方法

为提高数据分类的性能,提出了一种基于信息熵[1]的多分类器动态组合方法（EMDA）。此方法在多个UCI标准数据集上进行了测试,并与由集成学习算法—AdaBoost,训练出的各个基分类器的分类效果进行比较,证明了该算法的有效性。     

基于粗糙集约简的多分类器系统构造方法

多分类器系统是近年来兴起的一种有效的分类机制,为提高多分类器系统的分类精度,提出了一种基于粗糙集约简构造多分类器系统的机制,并从输入和输出两个角度对如何选择单个分类器进行了探讨。通过对4个UCI数据集进行验证,发现基于输出的选择融合方法得到了最好的分类效果。     

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.     

多分类器选择集成方法

针对目前人们对分类性能的高要求和多分类器集成实现的复杂性,从基分类器准确率和基分类器间差异性两方面出发,提出了一种新的多分类器选择集成算法。该算法首先从生成的基分类器中选择出分类准确率较高的,然后利用分类器差异性度量来选择差异性大的高性能基分类器,在分类器集成之前先对分类器集进行选择获得新的分类器集。在UCI数据库上的实验结果证明,该方法优于bagging方法,取得了很好的分类识别效果。     

基于差异性度量的多分类器集成系统设计

为了解决在分类器集成过程中分类性能要求高和集成过程复杂等问题,分析常规集成方法的优缺点,研究已有的分类器差异性度量方法,提出了筛选差异性尽可能大的分类器作为基分类器而构建的一个层级式分类器集成系统.构建不同的基分类器,选择准确率较高的备选,分析其差异性,选出差异大的分类器作为系统所需基分类器,构成集成系统.通过在UCI数据集上进行的试验,获得了很好的分类识别效果,验证了这种分类集成系统的优越性.     

动态加权投票的多分类器聚合

在使用多分类器系统时,一种流行的方法是采用简单的多数投票策略来聚合多分类器。然而,当各个独立的分类器的性能不统一时,这种简单的多数投票规则会对分类结果造成负面影响。引入一种新的动态加权函数来聚合多个分类器,动态加权函数通过增加分类结果距离样本最近的分类器的权值来提高分类器的性能。在UCI机器学习数据库中的几个现实问题数据集上的实验结果表明动态加权的多分类器聚合方法比简单的多数投票方法能取得更好的分类结果。     

基于多重判别分析的朴素贝叶斯分类器

通过分析朴素贝叶斯分类器的分类原理,并结合多重判别分析的优点,提出了一种基于多重判别分析的朴素贝叶斯分类器DANB(Discriminant Analysis Naive Bayesian classifier).将该分类方法与朴素贝叶斯分类器(Naive Bayesian classifier, NB)和TAN分类器(Tree Augmented Naive Bayesian classifier)进行实验比较,实验结果表明在大多数数据集上,DANB分类器具有较高的分类正确率.     

多分类器融合实现机型识别

针对空战目标识别中机型识别这一问题,提出了基于多分类器融合的识别方法。该方法以战术性能参数为输入,便于满足空战的实时性要求。通过广泛收集数据,得到机型识别的分类特征,选取分类特征的子集作为单分类器的特征,用BP网络设计单分类器,然后选用性能优良的和规则进行分类器融合,求得最终的决策。实验结果表明,多分类器融合的识别性能明显优于参与融合的分类器,也优于相同输入的单分类器。该方法的另一特点是能够进行缺省推理,因而有较强的抗干扰能力,适合真实战场环境的需要。     

基于贝叶斯的多窗口数据流分类研究

提出一种基于贝叶斯的多窗口数据流分类模型BCCDSMW对数据流进行分类。BC-CDSMW对时间窗口内的数据进行压缩。只有少量样本被保存,其他样本只保存少量统计量,以便在有限的空间上尽可能多地利用有效历史数据。目的是在适应概念漂移的前提下,利用多个时间段的数据学习生成单个贝叶斯分类器,使其能准确地反映当前数据流地实际情况,并且该分类器能快速地对未来数据分类处理。     

DEA-Based Piecewise Linear Discriminant Analysis

Nonlinear classification models have better classification performance than the linear classifiers. However, for many nonlinear classification problems, piecewise-linear discriminant functions can approximate nonlinear discriminant functions. In this study, we combine the algorithm of data envelopment analysis (DEA) with classification information, and propose a novel DEA-based classifier to construct a piecewise-linear discriminant function, in this classifier, the nonnegative conditions of DEA model are loosed and class information is added; Finally, experiments are performed using a UCI data set to demonstrate the accuracy and efficiency of the proposed model.     

Comprehensive evaluation of twin SVM based classifiers on UCI datasets

In the past decade, twin support vector machine (TWSVM) based classifiers have received considerable attention from the research community. In this paper, we analyze the performance of 8 variants of TWSVM based classifiers along with 179 classifiers evaluated in Fernandez-Delgado et al. (2014) from 17 different families on 90 University of California Irvine (UCI) benchmark datasets from various domains. Results of these classifiers are exhaustively analyzed using various performance criteria. Statistical testing is performed using Friedman Rank (FRank). Our experiments show that two least square TWSVM based classifiers (ILSTSVM_m, and RELS-TSVM_m) are the top two ranked methods among 187 classifiers and they significantly outperform all other classifiers according to Friedman Rank. Overall, this paper bridges the evaluational benchmarking gap between various TWSVM variants and the classifiers from other families. Codes of this paper are provided on authors’ homepages to reproduce the presented results and figures in this paper.     

Optimal ensemble construction via meta-evolutionary ensembles

In this paper, we propose a meta-evolutionary approach to improve on the performance of individual classifiers. In the proposed system, individual classifiers evolve, competing to correctly classify test points, and are given extra rewards for getting difficult points right. Ensembles consisting of multiple classifiers also compete for member classifiers, and are rewarded based on their predictive performance. In this way we aim to build small-sized optimal ensembles rather than form large-sized ensembles of individually-optimized classifiers. Experimental results on 15 data sets suggest that our algorithms can generate ensembles that are more effective than single classifiers and traditional ensemble methods.     

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.     

Combinations of weak classifiers

To obtain classification systems with both good generalization performance and efficiency in space and time, we propose a learning method based on combinations of weak classifiers, where weak classifiers are linear classifiers (perceptrons) which can do a little better than making random guesses. A randomized algorithm is proposed to find the weak classifiers. They are then combined through a majority vote. As demonstrated through systematic experiments, the method developed is able to obtain combinations of weak classifiers with good generalization performance and a fast training time on a variety of test problems and real applications. Theoretical analysis on one of the test problems investigated in our experiments provides insights on when and why the proposed method works. In particular, when the strength of weak classifiers is properly chosen, combinations of weak classifiers can achieve a good generalization performance with polynomial space- and time-complexity.     

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.     

数据包络分析模型的化工试验设计方法评价

数据包络分析(DEA)是以“相对效率评价”基于化工试验中反应物、生成物之间的投入、产出关系，探讨了化工试验设计，主要探讨了在化工试验设计中DEA作为评价正交试验设计方法的一种有效的分析工具的理论和应用。实例分析表明，将DEA方法运用于化工试验设计的评价具有计算简单，意义清楚的特点，是对正交试验设计的有益补充。     

Ensemble feature selection with the simple Bayesian classification

A popular method for creating an accurate classifier from a set of training data is to build several classifiers, and then to combine their predictions. The ensembles of simple Bayesian classifiers have traditionally not been a focus of research. One way to generate an ensemble of accurate and diverse simple Bayesian classifiers is to use different feature subsets generated with the random subspace method. In this case, the ensemble consists of multiple classifiers constructed by randomly selecting feature subsets, that is, classifiers constructed in randomly chosen subspaces. In this paper, we present an algorithm for building ensembles of simple Bayesian classifiers in random subspaces. The EFS_SBC algorithm includes a hill-climbing-based refinement cycle, which tries to improve the accuracy and diversity of the base classifiers built on random feature subsets. We conduct a number of experiments on a collection of 21 real-world and synthetic data sets, comparing the EFS_SBC ensembles with the single simple Bayes, and with the boosted simple Bayes. In many cases the EFS_SBC ensembles have higher accuracy than the single simple Bayesian classifier, and than the boosted Bayesian ensemble. We find that the ensembles produced focusing on diversity have lower generalization error, and that the degree of importance of diversity in building the ensembles is different for different data sets. We propose several methods for the integration of simple Bayesian classifiers in the ensembles. In a number of cases the techniques for dynamic integration of classifiers have significantly better classification accuracy than their simple static analogues. We suggest that a reason for that is that the dynamic integration better utilizes the ensemble coverage than the static integration.     

Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery

Recent researches in fault classification have shown the importance of accurately selecting the features that have to be used as inputs to the diagnostic model. In this work, a multi-objective genetic algorithm (MOGA) is considered for the feature selection phase. Then, two different techniques for using the selected features to develop the fault classification model are compared: a single classifier based on the feature subset with the best classification performance and an ensemble of classifiers working on different feature subsets. The motivation for developing ensembles of classifiers is that they can achieve higher accuracies than single classifiers. An important issue for an ensemble to be effective is the diversity in the predictions of the base classifiers which constitute it, i.e. their capability of erring on different sub-regions of the pattern space. In order to show the benefits of having diverse base classifiers in the ensemble, two different ensembles have been developed: in the first, the base classifiers are constructed on feature subsets found by MOGAs aimed at maximizing the fault classification performance and at minimizing the number of features of the subsets; in the second, diversity among classifiers is added to the MOGA search as the third objective function to maximize. In both cases, a voting technique is used to effectively combine the predictions of the base classifiers to construct the ensemble output. For verification, some numerical experiments are conducted on a case of multiple-fault classification in rotating machinery and the results achieved by the two ensembles are compared with those obtained by a single optimal classifier.     

基于稀疏表征多分类器融合的遮挡人脸识别

为了同时利用人脸局部信息, 提出一种基于稀疏表征多分类器融合的遮挡人脸识别方法。先对人脸进行多分辨率分块, 求取并根据各子块稀疏表征分类器的识别率确定其权重, 计算其后验概率估值, 最终利用加权融合准则进行多分类器融合识别。在AR和YaleA库的实验结果表明, 该算法结果比稀疏表征遮挡人脸识别的效果更好, 鲁棒性更高。