分类器组合技术的多样性研究

分类器组合技术可以提高模式识别的性能,受到了模式识别领域研究人员的广泛关注.实现成员分类器的多样性是提高分类器组合泛化能力主要手段.本文从成员分类器的生成介绍了实现成员分类器多样性的各种方法,同时介绍了度量成员分类器多样性的各种技术,并提出了一种如何训练多样性成员分类器的技术思路.     

基于混合多样性生成与修剪的集成单类分类算法

针对传统集成学习方法直接应用于单类分类器效果不理想的问题,该文首先证明了集成学习方法能够提升单类分类器的性能,同时证明了若基分类器集不经选择会导致集成后性能下降;接着指出了经典集成方法直接应用于单类分类器集成时存在基分类器多样性严重不足的问题,并提出了一种能够提高多样性的基单类分类器混合生成策略;最后从集成损失构成的角度拆分集成单类分类器的损失函数,针对性地构造了集成单类分类器修剪策略并提出一种基于混合多样性生成和修剪的单类分类器集成算法,简称为PHD-EOC。在UCI标准数据集和恶意程序行为检测数据集上的实验结果表明,PHD-EOC算法兼顾多样性与单类分类性能,在各种单类分类器评价指标上均较经典集成学习方法有更好的表现,并降低了决策阶段的时间复杂度。     

DMMP红外光谱的自动识别

介绍了模式识别技术在化学毒剂红外遥感监测领域应用的概况,探讨了线性分类器、分段线性分类器、反向传播人工神经网络(BP-ANN)分类器应用于红外光谱鉴别的可能性。用一个DMMP(甲基膦酸二甲酯)红外光谱数据样本集对上述三种分类器进行了实际的训练和鉴别性能预测,结果发现,分段线性分类器的性能优于另外两种分类器,鉴别率达到了80％以上。     

DMMP红外光谱的自动识别

介绍了模式识别技术在化学毒剂红外遥感监测领域应用的概况，探讨了线性分类器、分段线性分类器、反向传播人工神经网络(BP-ANN)分类器应用于红外光谱鉴别的可能性．用一个DMMP(甲基膦酸二甲酯)红外光谱数据样本集对上述三种分类器进行了实际的训练和鉴别性能预测，结果发现，分段线性分类器的性能优于另外两种分类器，鉴别率达到了80％以上．     

DWDM技术的多样性及其特点

本文主要介绍ＤＷＤＭ技术各功能模块和系统实现的技术多样性及其特点。     

Boosting优化决策树的带钢表面缺陷识别技术

基于图像信息的缺陷识别技术是带钢表面缺陷检测系统中的关健技术之一.通过采用单一的分类技术或者一步到位的创建分类器,对复杂带钢表面缺陷图像进行识别很难达到理想的效果.提出了用Boosting算法结合SLIQ决策树建立组合分类器来识别带钢表面缺陷的方法.Boosting算法通过适应性权重技术和带权重的投票方法,建立并组合多个功能互补的分类器,组合分类器通过优势互补的方法有效地提高单个分类器的性能;而SLIQ决策树算法的数据预排序和广度优先技术对大规模数据分类具有速度优势,适合于作为单个分类器的弱学习算法.对实际带钢表面缺陷数据集进行测试,Boosting优化SLIQ决策树的组合分类器对缺陷识别的准确率达到了90%以上.     

汉字识别研究的回顾

本文回顾了汉字识别研究的历史。根据模仿人类视觉模型，基于文字图像的统计模式识别方法是文字识别取得瞩目进展的基础。模式识别信息熵理论揭示了模式分类的信息过程和理论极限，本文讨论了从汉字图像中提取特征以及文字识别分类器设计和学习的各种方法。介绍了文本识别必须解决的文字切分，版面分析、理解和重构，及提高识别性能等重点问题，最后，总结了文字识别研究的重要进展和对今后的展望。     

基于多分类器投票组合的语音情感识别

为了提高语音情感的正确识别率,提出一种基于多分类器投票组合的语音情感识别新方法.在提取情感语音的韵律特征和音质特征基础上,利用投票方法将支持向量机、K近邻法和人工神经网络三种分类器构成组合分类器,实现对汉语生气、高兴、悲伤和惊奇4种主要情感类型的识别.实验结果表明,与使用单一分类器相比,组合分类器对语音情感的识别取得了87.4%的平均正确识别率,识别效果优于单一分类器.     

脱机手写体汉字识别综述

脱机手写体汉字识别是模式识别领域最具挑战性的课题之一。本文分析了近年来脱机手写体汉字识别的最新进展,讨论了脱机手写体汉字分割、特征提取和分类器设计等关键技术的各种主流方法,介绍了3种典型的汉字识别数据库,并提出了脱机手写体汉字识别的难点问题和今后发展的趋势,为该领域的研究者指明研究方向,共同促进脱机手写体汉字识别技术的发展。     

浅谈模式识别在图像处理中的应用

模式识别是指处理和分析表征事物或现象的各种形式的信息,从而实现描述、辨认、分类和解释事物或现象的一个过程.作为一个比较新的计算机应用领域的学科,已经经历了几十年的发展,本文对这个学科做了一个简单的综述,首先简单介绍了模式识别技术的发展历程,接着介绍了其研究进展和现状,同时介绍比较了几种模式识别的新方法,最后给出了本文的结论.     

Co-Occurrence Histogram Based Ensemble of Classifiers for Classification of Cervical Cancer Cells

To explore the potential of conventional image processing techniques in the classification of cervical cancer cells, in this work, a co-occurrence histogram method was employed for image feature extraction and an ensemble classifier was developed by combining the base classifiers, namely, the artificial neural network (ANN), random forest (RF), and support vector machine (SVM), for image classification. The segmented pap-smear cell image dataset was constructed by the k-means clustering technique and used to evaluate the performance of the ensemble classifier which was formed by the combination of above considered base classifiers. The result was also compared with that achieved by the individual base classifiers as well as that trained with color, texture, and shape features. The maximum average classification accuracy of 93.44% was obtained when the ensemble classifier was applied and trained with co-occurrence histogram features, which indicates that the ensemble classifier trained with co-occurrence histogram features is more suitable and advantageous for the classification of cervical cancer cells.     

基于K-medoids聚类的贝叶斯集成算法

朴素贝叶斯分类算法由于其计算高效在生活中应用广泛。本文根据集成算法的差异性特征,聚类算法聚类点的选择方式的可变性,提出了基于K-medoids聚类技术的贝叶斯集成算法,朴素贝叶斯的泛化性能得到了提升。首先,通过样本集训练出多个朴素贝叶斯基分类器模型;然后,为了增大基分类器之间的差异性,利用K-medoids算法对基分类器在验证集上的预测结果进行聚类;最后,从每个聚类簇中选择泛化性能最佳的基分类器进行集成学习,最终结果由简单投票法得出。将该算法应用于UCI数据集,并与其他类似算法进行比较可得,本文提出的基于K-medoids聚类的贝叶斯集成算法(NBKME)提高了数据集的分类准确率。     

基于随机化属性选择和邻域覆盖约简的集成学习

 提高分类模型的分类精度和可靠性是分类建模追求的目标.针对目前规则学习方法应用于分类时稳定性差以及分类精度低的问题,本文通过随机化邻域属性约简,搜索一组分类精度较高的属性子集,在不同的属性子集上采用邻域覆盖约简方法学习分类规则,得到多个规则集.最后通过简单投票融合不同规则集上的分类结果获得对象的类别.实验表明,基于随机化邻域约简的集成学习方法分类性能优于或与其它相关的分类器相当,并且在噪声扰动下具有更强的鲁棒性.     

Classifying gene expression data of cancer using classifier ensemble with mutually exclusive features

The explosion of DNA and protein sequence data in public and private databases has been encouraging interdisciplinary research on biology and information technology. Gene expression profiles are just sequences of numbers, and the necessity of tools analyzing them to get useful information has risen significantly. In order to predict the cancer class of patients from the gene expression profile, this paper presents a classification framework that combines a pair of classifiers trained with mutually exclusive features. The idea behind feature selection with nonoverlapping correlation is to encourage classifier ensemble, which consists of multiple classifiers, to learn different aspects of training data, so that classifiers can search in a wide solution space. Experimental results show that the classifier ensemble produces higher recognition accuracy than conventional classifiers.     

Ensembles of Neural Networks for Fault Diagnosis in Analog Circuits

A new neural network-based analog fault diagnosis strategy is introduced. Ensemble of neural networks is constructed and trained for efficient and accurate fault classification of the circuit under test (CUT). In the testing phase, the outputs of the individual ensemble members are combined to isolate the actual CUT fault. Prominent techniques for producing the ensemble are utilized, analyzed and compared. The created ensemble exhibit high classification accuracy even if the CUT has overlapping fault classes which cannot be isolated using a unitary neural network. Each neural classifier of the ensemble focuses on a particular region in the CUT measurement space. As a result, significantly better generalization performance is achieved by the ensemble as compared to any of its individual neural nets. Moreover, the selection of the proper architecture of the neural classifiers is simplified. Experimental results demonstrate the superior performance of the developed approach.     

Key-Attributes-Based Ensemble Classifier for Customer Churn Prediction

Recently, it has been seen that the ensemble classifier is an effective way to enhance the prediction performance. However, it usually suffers from the problem of how to construct an appropriate classifier based on a set of complex data, for example, the data with many dimensions or hierarchical attributes. This study proposes a method to constructe an ensemble classifier based on the key attributes. In addition to its high-performance on precision shared by common ensemble classifiers, the calculation results are highly intelligible and thus easy for understanding. Furthermore, the experimental results based on the real data collected from China Mobile show that the key-attributes-based ensemble classifier has the good performance on both of the classifier construction and the customer churn prediction.     

一种增强差异性的半监督协同分类算法

半监督学习中的Tri-Training算法打破了以往算法对充分冗余视图的限制,并通过利用三个分类器处理标记置信度和样本预测问题提高了标记效率.为进一步增强协同训练过程中分类器之间的差异性以提高性能,本文在其理论基础上提出了一种增强差异性的半监督协同分类算法.该算法利用三个不同的分类器进行学习;考虑到分类模型在更新过程中,可能会因随机抽样导致性能恶化,该算法利用基于标记类别的分层抽样法来对已标记样本集进行抽样,并通过基于分类正确率的加权投票法实现了分类器的集成,提高了预测准确率.本文通过实验对所提出算法与Tri-Training算法做了性能比较,实验结果表明本文所提出的方法在分类问题上具有较好的性能,验证了该算法的有效性和可行性.     

Parametric classification of multichannel averaged event-related potentials

This paper focuses on the systematic development of a parametric approach for classifying averaged event-related potentials (ERPs) recorded from multiple channels. It is shown that the parameters of the averaged ERP ensemble can be estimated directly from the parameters of the single-trial ensemble, thus, making it possible to design a class of parametric classifiers without having to collect a prohibitively large number of single-trial ERPs. An approach based on random sampling without replacement is developed to generate a large number of averaged ERP ensembles in order to evaluate the performance of a classifier. A two-class ERP classification problem is considered and the parameter estimation methods are applied to independently design a Gaussian likelihood ratio classifier for each channel. A fusion rule is formulated to classify an ERP using the classification results from all the channels. Experiments using real and simulated ERPs are designed to show that, through the approach developed, parametric classifiers can be designed and evaluated even when the number of averaged ERPs does not exceed the dimension of the ERP vector. Additionally, it is shown that the performance of a majority rule fusion classifier is consistently superior to the rule that selects a single best channel.