一种新的基于聚类的多分类器融合算法

提出了一种新的多分类器融合算法,该算法能找出各分类器在特征空间中局部性能较好的区域,并利用具有最优局部性能的分类器的输出作为最终的融合结果。首先,利用各分类器对训练样本进行分类,这样训练样本被划分为正确分类样本和错误分类样本两个集合;接着,对这两个样本集合分别进行聚类分析来划分特征空间,并计算各分类器在特征空间局部区域中的性能;在测试时,选择测试样本周围局部性能最优的分类器的输出作为最终的融合结果。基于ELENA数据集的实验显示了该算法的有效性。     

基于稀疏表示和决策融合的图像分类方法

利用多个稀疏表示分类器融合的决策信息对图像进行分类,可避免单个特征对图像分类的影响。提出一种自适应调节权重的多稀疏分类器融合图像分类方法。对原始图像分别提取3组不同特征,并训练出各自稀疏表示分类器;根据各个子分类器的准确率,通过迭代计算自适应确定各分类器最终权重;融合各子分类器的输出结果进行最终类别判断。基于Cifar-10图像数据集进行多组实验,结果表明,相对仅提取单特征的图像分类方法,该方法有效提高了图像分类准确率。     

选择性集成学习算法的研究

通过选择性集成可以获得比单个学习器和全部集成学习更好的学习效果,可以显著地提高学习系统的泛化性能。文中提出一种多层次选择性集成学习算法,即在基分类器中通过多次按权重进行部分选择,形成多个集成分类器,对形成的集成分类器进行再集成,最后通过对个集成分类器多数投票的方式决定算法的输出。针对决策树与神经网络模型在20个标准数据集对集成学习算法Ada—ens进行了实验研究,试验证明基于数据的集成学习算法的性能优于基于特征集的集成学习算法的性能,有更好的分类准确率和泛化性能。     

选择性集成学习算法的研究

通过选择性集成可以获得比单个学习器和全部集成学习更好的学习效果,可以显著地提高学习系统的泛化性能。文中提出一种多层次选择性集成学习算法,即在基分类器中通过多次按权重进行部分选择,形成多个集成分类器,对形成的集成分类器进行再集成,最后通过对个集成分类器多数投票的方式决定算法的输出。针对决策树与神经网络模型在20个标准数据集对集成学习算法Ada—ens进行了实验研究,试验证明基于数据的集成学习算法的性能优于基于特征集的集成学习算法的性能,有更好的分类准确率和泛化性能。     

基于PWKNN算法的风电系统故障诊断研究

为了准确诊断风电系统故障类别,基于改进加权k近邻的粒子群优化算法（PWKNN）提出一种新的诊断方法。PWKNN通过调整权重来反映特征的重要性,并利用距离判断策略计算出多类标分类的相同概率。采用粒子群优化算法（PSO）优化了PWKNN的权值和参数k,利用特征提取训练分类器,结合特征选择的Pearson相关系数来消除无关特征,从而减少分类器的输出时间。对300W风力发电机的四种分类状态进行测试,与传统分类器的比较表明,PWKNN具有更高的分类精度。特征选择可以将平均特征数量从16个减少到2.8个,输出时间可以减少61%。     

基于Bagging集成学习的字符识别方法

针对字符识别对象的多样性,提出了一种基于Bagging集成的字符识别模型,解决了识别模型对部分字符识别的偏好现象。采用Bagging采样策略形成不同的数据子集,在此基础上用决策树算法训练形成多个基分类器,用多数投票机制对基分类器预测结果集成输出。理论分析与仿真实验结果表明,所提模型相比其他分类方法具有更好的分类能力。     

基于集成主成分分析的人脸识别

设计了一种基于主成分分析的分类器集成方法。应用随机子空间法获得多个初始分类器,由它们的分类性能给出分类器的保留分值,从而确定它们的保留优先级别,最后由保留优先级别选择一组分类器组成集成。理论分析和在人脸数据库ORL上的实验结果表明,这种基于集成PCA的分类方法能够更好地对模式进行分类。     

一种基于投票机制的代表点选择算法

代表点选择是面向数据挖掘与模式识别的数据预处理的重要内容之一，是提高分类器分类正确率和执行效率的重要途径。提出了一种基于投票机制的代表点选择算法，该算法能使所得到的代表点尽可能分布在类别边界上，且投票选择机制易于排除异常点，减少数据量，从而有利于提高最近邻分类器的分类精度和效率。通过与多个经典的代表点选择算法的实验比较分析，表明所提出的基于投票机制的代表点选择算法在提高最近邻分类器分类精度和数据降低率上都具有一定的优势。     

基于AdaBoost的组合分类器在遥感影像分类中的应用*

运用组合分类器的经典算法AdaBoost将多个弱分类器-神经网络分类器组合输出,并引入混合判别多分类器综合规则,有效提高疑难类别的分类精度,进而提高分类的总精度.最后以天津地区ASTER影像为例,介绍了基于AdaBoost的组合分类算法,并在此基础上实现了天津地区的土地利用分类.分类结果表明,组合分类器能有效提高单个分类器的分类精度,分类总精度由81.13%提高到93.32%.实验表明基于AdaBoost的组合分类是遥感图像分类的一种新的有效方法.     

面向多类别模式分类问题的新型阴性选择算法

针对阴性选择算法缺乏高效的分类器生成机制和“过拟合”抑制机制的缺陷,提出了一种面向多类别模式分类的阴性选择算法CS-NSA。通过引入克隆选择机制,根据分类器的分类效果和刺激度对其进行自适应学习;针对多类别模式分类的“过拟合”问题,引入了检测器集合的修剪机制,增强了检测器的分类推广能力。对比实验结果证明：与著名的人工免疫分类器AIRS相比,CS-NSA体现出更高的正确识别率。     

A probabilistic model of classifier competence for dynamic ensemble selection

The concept of a classifier competence is fundamental to multiple classifier systems (MCSs). In this study, a method for calculating the classifier competence is developed using a probabilistic model. In the method, first a randomised reference classifier (RRC) whose class supports are realisations of the random variables with beta probability distributions is constructed. The parameters of the distributions are chosen in such a way that, for each feature vector in a validation set, the expected values of the class supports produced by the RRC and the class supports produced by a modelled classifier are equal. This allows for using the probability of correct classification of the RRC as the competence of the modelled classifier. The competences calculated for a validation set are then generalised to an entire feature space by constructing a competence function based on a potential function model or regression. Three systems based on a dynamic classifier selection and a dynamic ensemble selection (DES) were constructed using the method developed. The DES based system had statistically significant higher average rank than the ones of eight benchmark MCSs for 22 data sets and a heterogeneous ensemble. The results obtained indicate that the full vector of class supports should be used for evaluating the classifier competence as this potentially improves performance of MCSs.     

An approach to the automatic design of multiple classifier systems

Multiple classifier systems (MCSs) based on the combination of outputs of a set of different classifiers have been proposed in the field of pattern recognition as a method for the development of high performance classification systems. Previous work clearly showed that multiple classifier systems are effective only if the classifiers forming them are accurate and make different errors. Therefore, the fundamental need for methods aimed to design “accurate and diverse” classifiers is currently acknowledged. In this paper, an approach to the automatic design of multiple classifier systems is proposed. Given an initial large set of classifiers, our approach is aimed at selecting the subset made up of the most accurate and diverse classifiers. A proof of the optimality of the proposed design approach is given. Reported results on the classification of multisensor remote sensing images show that this approach allows the design of effective multiple classifier systems.     

Multiple classifier systems for classification of multifrequency PolSAR images with limited training samples

ABSTRACT

The aim of this article is to improve land-cover classification accuracy from multifrequency full-polarimetric synthetic aperture radar (PolSAR) observations using multiple classifier systems (MCSs) when limited training samples are available. Two types of popular MCSs, tree-based MCSs and neural-based MCSs, were compared with individual decision tree (DT) and neural network methods. Moreover, an objective majority voting (OMV) was proposed and compared with majority voting (MV) and weighted MV (WMV) to fuse the results of the MCSs. Experimental tests were performed on three benchmark PolSAR data sets with different frequencies (X, C, and L) over the San Francisco Bay, CA. The results indicated (1) tree-based MCSs and neural-based MCSs, in general, produced higher overall, producer?s and user?s accuracies than the related individual methods, i.e. DT and NN, with limited training samples; (2) tree-based MCSs were also often more accurate and much faster than neural-based MCSs; (3) regarding robustness, among the MCSs, random forest showed higher stability while bagging showed lower stability in the classification of three PolSAR data sets; (4) the OMV proposed in this article usually outperformed its competitors, i.e. MV and WMV; (5) the results obtained by the methods from the C-band data set were more accurate and more reliable than those obtained from the X- and L-band data sets.     

自主空中加油多摄像机近距相对导航与控制

针对伸缩套管方式下自主空中加油(AAR)对接与位置保持过程,提出了一个基于多摄像机系统(MCS)的相对导航与控制方案.该方案中的位姿估计算法通过结合3D-3D位姿估计技术和N点透视技术,能够充分利用多摄像机系统的宽视野和冗余测量信息.使用卡尔曼滤波算法对视觉估计数据进行滤波,并与速度测量数据进行融合,以提高视觉导航算法的精度和鲁棒性.为抑制外部风扰动,设计了一个基于动态对策理论的最优相对轨迹跟踪控制律.仿真结果表明提出的方案能够满足空中加油要求.     

Design and optimization of laminated piezoresistive microcantilever sensors

Microcantilevers-based sensors (MCSs) are a new approach to detecting and measuring physical, chemical, and biological signals in the nano- to femto-range level. Piezoresistive readout systems for MCSs have the advantages of full integration, low cost, ease of use, and the capability of manipulating large arrays. This paper presents a design method for laminated piezoresistive MCSs to obtain optimal performance by optimizing the dimensions of the microcantilevers and the doping concentration of the piezoresistors. Laminated theory was employed to deduce the closed-form solutions to static stress and natural frequency. Expressions for predicting sensitivity and resolution were derived by combining stress distribution with power densities of 1/f noise and Johnson noise. Finite element method (FEM) was performed to verify the theoretical results. The thickness of the laminated MCSs and the doping concentration were optimized by using static analyses and power densities of noise to generate the best sensitivity and resolution. A method based on non-linear programming is given to facilitate the solving process. These methods and some conclusions are also applicable to developing other types of piezoresistive sensors that use laminated structures.     

一种搜索编码法及其在监督分类中的应用

纠错输出码作为监督分类领域中的一个新的研究方向,是提高分类器泛化能力的一种有效方法,但目前还没有通用的确定性编码方法.分析了现有纠错输出码的性质,提出一种搜索编码法,该方法通过对整数空间的顺序搜索,获得满足任意类别数目与最小汉明距离要求的输出码;然后探讨了基于搜索编码的监督分类技术.对简单贝叶斯与BP神经网络算法进行实验,结果表明,搜索编码法可作为一种通用的编码方法用于提高监督分类器的泛化能力.     

An innovative analyser for multi-classifier e-mail classification based on grey list analysis

In this paper, we propose a new technique of e-mail classification based on the analysis of grey list (GL) from the output of an integrated model, which uses multi-classifier classification ensembles of statistical learning algorithms. The GL is the output of a list of classifiers which are not categorized as true positive (TP) nor true negative (TN) but in an unclear status. Many works have been done to filter spam from legitimate e-mails using classification algorithms and substantial performance has been achieved with some amount of false-positive (FP) tradeoffs. However, in spam filtering applications the FP problem is unacceptable in many situations, therefore it is critical to properly classify e-mails in the GL. Our proposed technique uses an innovative analyser for making decisions about the status of these e-mails. It has been shown that the performance of our proposed technique for e-mail classification is much better than the existing systems, in terms of reducing FP problems and improving accuracy.     

Mapping vegetation and land cover in a large urban area using a multiple classifier system

Vegetation and land-cover information is critical for sustainable environmental management in urban areas. Remote sensing has increasingly been used to derive such information, yet it has been challenged by the spectral and spatial complexity in the urban environment. In this study, we developed a multiple classifier system (MCS) to help improve remote-sensing-based vegetation and land-cover mapping in a large metropolitan area. MCSs, although considered as an emerging hot topic and a promising trend in pattern recognition, have not received the attention it deserves in the remote-sensing community. Our work consisted of several components. First, we identified a group of commonly used pattern recognizers from different families of statistical learning algorithms as base classifiers. Then, we implemented them to derive land-cover information from a satellite image covering the study site. Last, we adopted a weighting and combination method to generate the final map. Results indicate that there is statistically significant difference in the classification accuracy between the MCS developed and each base classifier considered. Comparing with the base classifiers, the MCS produced not only about 5–8% higher overall classification accuracy but also the most stable categorical accuracies. Moreover, the MCS generated a larger accuracy improvement for spectrally complex classes than for relatively homogenous ones, suggesting its comparative advantage in reducing classification errors caused by class ambiguity. The novelties of our work are with the demonstration of how MCSs can be operationally used to improve image classification from large remote sensor data sets with complex patterns and with the insight into the behaviour of MCSs in relation to the complexity of individual classes. These findings can help promote the use of MCSs as an emerging premier approach for image classification by the remote-sensing community.     

Imposed target based modification of Taguchi method for feature optimisation with application in arrhythmia beat detection

Development of an expert system for clinical application includes automation in diagnosis of abnormality and patient monitoring based on features derived from continuous data set. This paper presents a novel method for feature optimization and classification of electrocardiogram (ECG) for arrhythmia analysis. A feature set optimization technique can reduce the classification hazard by selecting few comprehensive features to cater all kind of abnormalities under consideration. Proposed work deals with ranking and selection of an optimized pair of features using Taguchi method from eleven possible features normally used for characterizing arrhythmic beats like left bundle branch (LBBB), right bundle branch (RBBB) and premature ventricular contraction (PVC) are compared to normal beats. An imposed target based modification of Taguchi method is also suggested for the systems where the output is not pre-defined as in the case of biomedical applications. The proposed method is advantageous for the expert systems in which individual identity of the features are to be stored while reducing the dimensionality of the feature set. Multiclass Navis Bayes classifier is used to classify the beats in a single run and good performance parameters are obtained as reported in the result section.     

Multiple Classifier Systems: Software Engineered,Automatically Modular Leading to a Taxonomic Overview

One vigorous branch of research aimed at improving the performance of pattern recognition systems explores the possibilities for exploiting the differences between a set of variously configured classifiers. This is the field of Multiple Classifier Systems (MCS), and it is based on the premise that it ought to be possible to organise and exploit the strengths and weaknesses of individual classifiers such that the MCS performance is superior to that of any of its components. Important concerns are the efficiency of multiple classifier construction, and the effectiveness of the final MCS. What property or properties of the set of multiple classifiers are being exploited by the various decision strategies, and how are the desired properties to be realised within a set of classifiers? Analogous ideas and strands of research have arisen within both software engineering and neural computing. This paper surveys these other two fields from an MCS perspective with the goal of revealing useful results that should have direct application for current work in MCS. In particular, the survey opens up new possibilities within MCS as well as provides new formal bases for the central underlying ideas, such as classifier independence and diversity. The exploration of diversity is extended to a consideration of MCSs in which the component classifiers are specialised for classification of an identifiable subset of the complete classification problem. Results are given of an empirical study of an automatic specialisation strategy that demonstrates the predictive use of several diversity measures. Finally, a taxonomy is presented as a unifying framework for the many varieties of MCSs. Received: 15 November 2000, Received in revised form: 15 November 2001, Accepted: 13 December 2001