多分类器组合及其应用

1 引言传统的模式识别系统通常只使用样本的某种单一特征描述和特定的一个分类器来进行分类。这种系统对于类别数较大、输入样本带噪声的问题很难获得好的分类效果。近来发现不同的特征描述、不同的分类器在分类性能上存在着彼此互补的现象,因此同时使用多种特征描述和多个分类器可能提高分类精确性。目前,多分类器组合的研究吸引了学者们广泛的注意,并     

基于NN多分类器组合的入侵检测方法

本文根据传统的入侵检测方法误报率高、U2R和R2L攻击检测率低等缺点,提出了一种基于神经网络多分类器组合的入侵检测方法.实验结果表明,该方法不仅能够有效地提高检测率,特别是U2R和R2L等攻击具有较好的检测能力.因此,本文提出的基于神经网络多分类器组合的入侵检测方法是有效和实用的.     

多分类器组合研究

文章提出了一种多分类器的组合方法,它利用了参与组合的分类器提供的度量层次上的两类信息:对训练样本的决策信息;对待识样本的决策信息。首先对这两类信息进行集成,进而给出了组合分类器的判定规则。用该方法对手写体汉字作分类识别,实验结果显示,较之其它几种方法,它有更高的正确识别率。     

神经网络分类器的组合

利用神经网络分类器组合,对手写体数字识别问题进行了研究。通过对同一训练样本集抽取不同的特征集合,从而获得不同的神经网络分类器。对这些分类器的分类结果组合得到最终的分类结果。提出性能函数PF（S,T）用来确定阈值S,T,从而获得错误率与拒识率间的最佳平衡,实验结果表明,此种分类器组合方法能根据不同应用的要求,自动地选取性能函数中的参数,减少分类错误率,提高识别的可靠性。     

一种新的双层多分类器组合算法

多分类器组合是解决复杂模式识别问题的有效办法。文章提出了一种新的双层多分类器组合算法,首先利用分类对象的主次特征构建了多个差异的融合方案,然后对这些融合方案进行最终的组合决策。实验结果表明,对于复杂分类问题,本文算法具有较高的正确识别率。     

多特征和多分类器组合的湿地遥感影像分类

为了适应湿地遥感影像分类,选择了湿地影像的典型特征,提出了一种组合多分类器的湿地遥感分类方法。提取湿地遥感影像的独立分量、纹理、湖泊透明度、归一化水体指数、绿度指数和湿度分量特征;选择样本对最小欧氏距离、光谱夹角填图、贝叶斯和支持向量机分类器进行训练学习。根据各分类器的混淆矩阵对其赋权值,检验样本是否满足正态分布;根据权值和假设检验结果构建组合分类器决策网络。实验表明该方法较传统湿地分类方法具有更好的性能和更高的精度。     

基于多分类器组合的人脸识别

根据人类认知的规律,文中提出了一种基于整体和局部特征组合的人脸识别方法。奇异值特征是一种比较有效的代数特征,文中提取了整个人脸、双眼以及嘴部的奇异值特征。在组合过程中,提出了一种改进的将距离转换为后验概率估计值的方法,该方法既缩减了单一分类器的可能的模式类别,又对各分类器的输出进行了加权。实验结构表明文中方法是有效的。     

基于多分类器组合的多角度彩色人脸图像检测

提出了基于神经网络和隐马尔可夫模型组合的彩色人脸图像检测方法 .根据归一化后的彩色图像的色度彩色分量直方图将图像粗分割成若干幅二值图像 ;在亮度图像上 ,以上述二值图像为掩模进行多分辨率的旋转不变性人脸检测 .在人脸检测时 ,本文分两步 :第一步先用神经网络来确定人脸的旋转角度 ,然后对旋正后的图像运用识别人脸奇异值特征的隐马尔可夫模型进行验证 .实验结果表明 ,本文算法是有效的     

集成学习的多分类器动态组合方法

为了提高数据的分类性能,提出一种集成学习的多分类器动态组合方法(DEA)。该方法在多个UCI标准数据集上进行测试,并与文中使用的基于Adaboost算法训练出的各个成员分类器的分类效果进行比较,证明了DEA的有效性。     

基于多分类器组合的笔迹验证

运用多分类器组合技术和模糊技术将多种笔迹鉴别方法按一定规则进行融合,针对笔迹鉴别中的笔迹验证问题进行应用。实验结果表明,融合后笔迹验证准确率有大幅的提高     

Ensemble of Evolving Neural Networks in Classification

The ensemble of evolving neural networks, which employs neural networks and genetic algorithms, is developed for classification problems in data mining. This network meets data mining requirements such as smart architecture, user interaction, and performance. The evolving neural network has a smart architecture in that it is able to select inputs from the environment and controls its topology. A built-in objective function of the network offers user interaction for customized classification. The bagging technique, which uses a portion of the training set in multiple networks, is applied to the ensemble of evolving neural networks in order to improve classification performance. The ensemble of evolving neural networks is tested by various data sets and produces better performance than both classical neural networks and simple ensemble methods.     

Cascade Generalization

Using multiple classifiers for increasing learning accuracy is an active research area. In this paper we present two related methods for merging classifiers. The first method, Cascade Generalization, couples classifiers loosely. It belongs to the family of stacking algorithms. The basic idea of Cascade Generalization is to use sequentially the set of classifiers, at each step performing an extension of the original data by the insertion of new attributes. The new attributes are derived from the probability class distribution given by a base classifier. This constructive step extends the representational language for the high level classifiers, relaxing their bias. The second method exploits tight coupling of classifiers, by applying Cascade Generalization locally. At each iteration of a divide and conquer algorithm, a reconstruction of the instance space occurs by the addition of new attributes. Each new attribute represents the probability that an example belongs to a class given by a base classifier. We have implemented three Local Generalization Algorithms. The first merges a linear discriminant with a decision tree, the second merges a naive Bayes with a decision tree, and the third merges a linear discriminant and a naive Bayes with a decision tree. All the algorithms show an increase of performance, when compared with the corresponding single models. Cascade also outperforms other methods for combining classifiers, like Stacked Generalization, and competes well against Boosting at statistically significant confidence levels.     

权重自适应调整的多分类器集成判决及其在文本分类中的应用

1 引言近年来,多分类器的组合方法已成为模式识别研究的热点问题,并已在模式识别的多个应用方面,如字符识别、目标识别、文本分类等领域获得了较好的应用效果。多分类器组合方法的基本假设是:对一个需要专家进行的任务,k个专家个人判断的有效组合应该优于个人的判断。利用具有不同特性和性能的多分类器,通过进行有效的组合可以获得更高的模式识别性能。     

Bayesian network classifiers for probability-based metrics

A large number of distance metrics have been proposed to measure the difference of two instances. Among these metrics, Short and Fukunaga metric (SFM) and minimum risk metric (MRM) are two probability-based metrics which are widely used to find reasonable distance between each pair of instances with nominal attributes only. For simplicity, existing works use naive Bayesian (NB) classifiers to estimate class membership probabilities in SFM and MRM. However, it has been proved that the ability of NB classifiers to class probability estimation is poor. In order to scale up the classification performance of NB classifiers, many augmented NB classifiers are proposed. In this paper, we study the class probability estimation performance of these augmented NB classifiers and then use them to estimate the class membership probabilities in SFM and MRM. The experimental results based on a large number of University of California, Irvine (UCI) data-sets show that using these augmented NB classifiers to estimate the class membership probabilities in SFM and MRM can significantly enhance their generalisation ability.     

基于专家域的多层分类器融合

论文提出了一种基于专家域的多层分类器融合模型,专家指不同专长之单分类器。模型思想来自医院诊断流程,模型首先训练n个专家,之后将样本空间按专家专长划分专家域。对于待测样本,先将样本指派到合适的专家域,然后再由指定的专家对样本进行分类。用这种算法对UCI的标准数据集进行分类,实验结果显示,该算法得到比其他算法更低的分类误差,显著提高了分类器的性能。     

基于网格服务的分布式数据挖掘

在分析传统分布式数据挖掘平台不足的基础上,结合网格服务的思想,提出了基于网格服务的分布式数据挖掘平台,同时在该平台上,实现了分布式BP网络分类算法（GBPC-GS）。仿真实验表明,与单机环境相比,随着网格节点数增加,算法的平均耗时明显下降,同时CPU的负载也下降了约40%。     

A class of classification and regression methods by multiobjective programming

An extensive review for the recent developments of multiple criteria linear programming data mining models is provided in this paper. These researches, which include classification and regression methods, are introduced in a systematic way. Some applications of these methods to real-world problems are also involved in this paper. This paper is a summary and reference of multiple criteria linear programming methods that might be helpful for researchers and applications in data mining.     

基于全信息矩阵的多分类器集成方法

自动文本分类是提高信息利用效率和质量的有效方法,而多分类器的有效组合能够得到更高的分类准确率.给出了样本集在多分类器下的全信息矩阵概念,并提出一种权重自适应调整的多分类器集成方法.该方法能够自适应地选择分类器组合及确定分类器权重,并利用分类统计信息指导分类结果的集成判决.通过在标准文本集Reuters-21578上的实验表明:该方法能从查准率和查全率两方面提高文本分类的整体性能,同时表明了该方法的有效性.