Bayesian texture classification and retrieval based on multiscale feature vector

This paper proposes a supervised multiscale Bayesian texture classifier. The classifier exploits the dual-tree complex wavelet transform (DT-CWT) to obtain complex-valued multiscale representations of training texture samples for each texture class. The high-pass subbands of DT-CWT decomposition of a texture image are used to form a multiscale feature vector representing magnitude and phase features. For computational efficiency, the dimensionality of feature vectors is reduced using principal component analysis (PCA). The class conditional probability density function of low-dimensional feature vectors for each texture class is then estimated by using Parzen-window estimate with identical Gaussian kernels and is used to represent the texture class. A query texture image is classified as the corresponding texture class with the highest a posteriori probability according to a Bayesian inferencing. The superior performance and robustness of the proposed classifier is demonstrated for classifying texture images from image databases. The proposed multiscale texture feature vector extracted from both magnitude and phase of DT-CWT subbands of a query image is also shown to be effective for texture retrieval.     

基于单簇聚类的数据描述

文中提出了一种基于单簇可能性C-均值聚类(Possibilistjc C-Means,PCM)的数据描述方法并用于单分类.训练时,其首先进行P1M(PCM,C值取1)聚类,得到所有训练样本对目标类的隶属度;然后设置隶属度阈值,形成相应的数据描述进行单分类.分类时,计算新样本对目标类的隶属度,若其隶属度小于该阈值则判为异常,否则为正常.该方法和当前流行的支持向量域数据描述方法以及Parzen方法窗具有类似的参数配置和相当的分类性能,由此提供了另一种单分类学习算法.值得指出的是,尽管是PCM的一个特例,但P1M拥有PCM一般不具备的全局最优特性,而该特性对解决实际问题十分重要.     

一种新高斯过程分类算法

由于需要利用高斯函数逼近潜变量函数的后验概率, 传统高斯过程分类算法通常都存在计算复杂度高的问题. 对此, 提出一种新高斯过程分类算法. 该算法的基本思想为: 首先, 利用Parzen 窗方法估计出每个训练样本的后验概率; 然后, 通过所得到的后验概率将原始分类问题变换为回归问题; 进而分析地得到潜变量函数后验概率的显式表达式, 以避免逼近后验概率所面临的高计算复杂度问题. 仿真实验结果表明, 所提出的算法在分类精度上优于已有的高斯过程分类算法.

     

基于后验概率的不平衡数据集特征选择算法

针对不平衡数据集,提出一种基于后验概率的特征选择算法。该算法引入基于Parzen-window方法估算的不均衡因子,并以Tomek links中点为初始值进行迭代,找出满足后验概率相等的判别边界点,通过对这些点法向量进行投影计算得到各特征的权值。实验表明,对于不平衡数据集,该算法在不降低分类器总体性能的基础上,不仅可以有效降低维度,节省计算开销,而且能够避免常规特征选择算法用于不平衡数据时忽视小类的缺点。     

Application of the Gaussian mixture model to drug dissolution profiles prediction

In this paper, the radial basis function-based Gaussian mixture model (GMM) is applied to model and predict drug dissolution profiles in a time-series approach. The Parzen-window method is embedded into the GMM for determining whether the network predictions are derived from interpolation or extrapolation of the training data. A benchmark study on time-series prediction is first used to evaluate and compare the GMM performance with those from other models. The GMM is then used to predict dissolution profiles of a matrix-controlled release theophylline pellet preparation. Performance of the GMM is assessed using the difference and similarity factors, as recommended by the United States Food and Drug Administration for dissolution profile comparison. In addition, bootstrapping is employed to estimate the confidence intervals of the network predictions. The experimental results are analyzed and compared, and implications of the GMM for pharmaceutical product formulation tasks are discussed.     

汉字识别中以词为分类单位的分类器研究

汉字识别中,以往的分类器设计都是以字为单位的“字分类器”。字分类器的输出总是与待识字结构相似的一个侯选字集合。这是使后级识别容易产生误识的主要原因。为克服字分类器的缺点,本文给出了以词为单位的词分类器设计的策略与方法,并实验验证了词分类器在分类率及分类速度方面均优于字分类器。     

An artificial immune classifier for credit scoring analysis

The primary concern of the rating policies for a banking industry is to develop a more objective, accurate and competitive scoring model to avoid losses from potential bad debt. This study proposes an artificial immune classifier based on the artificial immune network (named AINE-based classifier) to evaluate the applicants’ credit scores. Two experimental credit datasets are used to show the accuracy rate of the artificial immune classifier. The ten-fold cross-validation method is applied to evaluate the performance of the classifier. The classifier is compared with other data mining techniques. Experimental results show that for the AINE-based classifier in credit scoring is more competitive than the SVM and hybrid SVM-based classifiers, except the BPN classifier. We further compare our classifier with other three AIS-based classifiers in the benchmark datasets, and show that the AINE-based classifier can rival the AIRS-based classifiers and outperforms the SAIS classifier when the number of attributes and classes increase. Our classifier can provide the credit card issuer with accurate and valuable information of credit scoring analyses to avoid making incorrect decisions that result in the loss of applicants’ bad debt.     

贝叶斯分类器的判别式参数学习

为了提高贝叶斯分类器的分类性能,针对贝叶斯网络分类器的构成特征,提出一种基于参数集成的贝叶斯分类器判别式参数学习算法PEBNC。该算法将贝叶斯分类器的参数学习视为回归问题,将加法回归模型应用于贝叶斯网络分类器的参数学习,实现贝叶斯分类器的判别式参数学习。实验结果表明,在大多数实验数据上,PEBNC能够明显提高贝叶斯分类器的分类准确率。此外,与一般的贝叶斯集成分类器相比,PEBNC不必存储成员分类器的参数,空间复杂度大大降低。     

分类器组合在心电图分类中的应用

心电图是诊断心血管疾病的重要依据。提出将两个分类器（贝叶斯分类器和支持向量机分类器）进行组合,对五种心电图疾病建立分类模型,并利用麻省理工学院（MIT-BIH）的心电图数据库中的数据进行训练和测试,实验结果表明,经过组合过的分类器的分类正确率比单个贝叶斯分类器和单个支持向量机分类器的正确率要高。     

A comparison of multiple classifier combinations using different voting-weights for remote sensing image classification

Remote sensing image classification is a common application of remote sensing images. In order to improve the performance of Remote sensing image classification, multiple classifier combinations are used to classify the Landsat-8 Operational Land Imager (Landsat-8 OLI) images. Some techniques and classifier combination algorithms are investigated. The classifier ensemble consisting of five member classifiers is constructed. The results of every member classifier are evaluated. The voting strategy is experimented to combine the classification results of the member classifier. The results show that all the classifiers have different performances and the multiple classifier combination provides better performance than a single classifier, and achieves higher overall accuracy of classification. The experiment shows that the multiple classifier combination using producer’s accuracy as voting-weight (MCC_mod2 and MCC_mod3) present higher classification accuracy than the algorithm using overall accuracy as voting-weight (MCC_mod1).And the multiple classifier combinations using different voting-weights affected the classification result in different land-cover types. The multiple classifier combination algorithm presented in this article using voting-weight based on the accuracy of multiple classifier may have stability problems, which need to be addressed in future studies.     

A self-constructing cascade classifier with AdaBoost and SVM for pedestriandetection

In this paper, we propose a cascade classifier combining AdaBoost and support vector machine, and applied this to pedestrian detection. The pedestrian detection involved using a window of fixed size to extract the candidate region from left to right and top to bottom of the image, and performing feature extractions on the candidate region. Finally, our proposed cascade classifier completed the classification of the candidate region. The cascade-AdaBoost classifier has been successfully used in pedestrian detection. We have improved the initial setting method for the weights of the training samples in the AdaBoost classifier, so that the selected weak classifier would be able to focus on a higher detection rate other than accuracy. The proposed cascade classifier can automatically select the AdaBoost classifier or SVM to construct a cascade classifier according to the training samples, so as to effectively improve classification performance and reduce training time. In order to verify our proposed method, we have used our extracted database of pedestrian training samples, PETs database, INRIA database and MIT database. This completed the pedestrian detection experiment whose result was compared to those of the cascade-AdaBoost classifier and support vector machine. The result of the experiment showed that in a simple environment involving campus experimental image and PETs database, both our cascade classifier and other classifiers can attain good results, while in a complicated environment involving INRA and MIT database experiments, our cascade classifier had better results than those of other classifiers.     

Theoretical and experimental analysis of a two-stage system forclassification

We consider a popular approach to multicategory classification tasks: a two-stage system based on a first classifier with rejection followed by a nearest-neighbor classifier. Patterns which are not rejected by the first classifier are classified according to its output. Rejected patterns are passed to the nearest-neighbor classifier together with the top-h ranking classes returned by the first classifier. The nearest-neighbor classifier, looking at patterns in the top-h classes, classifies the rejected pattern. An editing strategy for the nearest-neighbor reference database, controlled by the first classifier, is also considered. We analyze this system. Moreover, we formally relate the response time of the system to the rejection rate of the first classifier and to the other system parameters. The error-response time trade-off is also discussed. Finally, we experimentally study two instances of the system applied to the recognition of handwritten digits. In one system, the first classifier is a fuzzy basis functions network, while in the second system it is a feed-forward neural network. Classification results as well as response times for different settings of the system parameters are reported for both systems     

基于迁移学习的多源数据隐私保护方法研究

隐私保护的多源数据分析是大数据分析的研究热点,在多方隐私数据中学习分类器具有重要应用。提出两阶段的隐私保护分析器模型,首先在本地使用具有隐私保护性的PATE-T模型对隐私数据训练分类器;然后集合多方分类器,使用迁移学习将集合知识迁移到全局分类器,建立一个准确的、具有差分隐私的全局分类器。该全局分类器无需访问任何一方隐私数据。实验结果表明,全局分类器不仅能够很好地诠释各个本地分类器,而且还可以保护各方隐私训练数据的细节。     

一种模糊分类器集成的方法

提出了一种基于模糊积分的模糊分类器集成的方法，该方法能在模糊分类器生成过程中，进一步减少主观因素的参与成份，使分类模器具有更好的稳定性和更高的分类识别率。给出了基于隶属度矩阵的模糊积分密度确定方法，介绍了基于模糊积分的分类器集成算法。用权威的数据集作为实验数据集，将提出方法与已有的分类器集成方法进行实验比较，评测了所提出方法的有效性。     

Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases

Abstract: The aim of this research was to compare classifier algorithms including the C4.5 decision tree classifier, the least squares support vector machine (LS-SVM) and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from pattern electroretinography signals. The pattern electroretinography signals were obtained by electrophysiological testing devices from 106 subjects who were optic nerve and macular disease subjects. In order to show the test performance of the classifier algorithms, the classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, confusion matrix and 10-fold cross-validation have been used. The classification results obtained are 85.9%, 100% and 81.82% for the C4.5 decision tree classifier, the LS-SVM classifier and the AIRS classifier respectively using 10-fold cross-validation. It is shown that the LS-SVM classifier is a robust and effective classifier system for the determination of macular and optic nerve diseases.     

双层分类策略及其实现算法*

针对常见分类算法在全局和局部区域性能不一致的问题,提出了双层分类策略及其实现算法。双层分类策略的思想是离线地建立全局分类器,当全局分类器决策信用度低于指定阈值时,在线生成局部分类器进行决策修正。实现算法以支持向量机(support vector machine,SVM)和模糊分类器(fuzzy classifier)作为全局与局部分类器,命名为SFC。为全局分类器定义了SVM决策信用度的评估机制,并以此给出局部分类器的启动条件。为局部分类器设计了基于新测度的模糊隶属度函数完成决策修正。实验结果表明,SFC     

A hybrid discriminative/generative approach to protein fold recognition

There are two standard approaches to the classification task: generative, which use training data to estimate a probability model for each class, and discriminative, which try to construct flexible decision boundaries between the classes. An ideal classifier should combine these two approaches. In this paper a classifier combining the well-known support vector machine (SVM) classifier with regularized discriminant analysis (RDA) classifier is presented. The hybrid classifier is used for protein structure prediction which is one of the most important goals pursued by bioinformatics. The obtained results are promising, the hybrid classifier achieves better result than the SVM or RDA classifiers alone. The proposed method achieves higher recognition ratio than other methods described in the literature.     

基于覆盖算法的两层结构分类器设计

分类器设计是模式识别系统中的关键步骤之一。在目前的许多设计方法中,分类器大多采用的是单层结构,即直接将输入模式映射为识别出来的结果,这类结构虽然简单直观,但是往往难于发挥分类器设计算法的最大性能。文中从分类器的结构方面考虑,提出了一种基于覆盖算法的两层结构分类器的设计方法,并且与单层结构分类器做了实验分析对比,得出了在不明显增加构造复杂度的情况下两层结构的设计大大改善了分类器的性能。     

A novel multiplex cascade classifier for pedestrian detection

Reliable pedestrian detection is of great importance in visual surveillance. In this paper, we propose a novel multiplex classifier model, which is composed of two multiplex cascades parts: Haar-like cascade classifier and shapelet cascade classifier. The Haar-like cascade classifier filters out most of irrelevant image background, while the shapelet cascade classifier detects intensively head-shoulder features. The weighted linear regression model is introduced to train its weak classifiers. We also introduce a structure table to label the foreground pixels by means of background differences. The experimental results illustrate that our classifier model provides satisfying detection accuracy. In particular, our detection approach can also perform well for low resolution and relatively complicated backgrounds.     

Classifier ensembles to improve the robustness to noise of bearing fault diagnosis

In this paper, we perform a noise analysis to assess the degree of robustness to noise of a neural classifier aimed at performing multi-class diagnosis of rolling element bearings. We work on vibration signals collected by means of two accelerometers and we consider ten levels of noise, each of which characterized by a different signal-to-noise ratio ranging from 40.55 to ?11.35 db. We classify the noisy signals by means of a neural classifier initially trained on signals without noise and then we repeat the training process with signals affected by increasing levels of noise. We show that adding noisy signals to the training set we can significantly increase the classification accuracy of a single classifier. Finally, we apply the two most used strategies to combine classifiers: classifier fusion and classifier selection, and show that, in both cases, we can significantly increase the performance of the single best classifier. In particular, classifier selection achieves the best results for low and medium levels of noise, while classifier fusion is the most accurate for high levels of noise. The analysis presented in the paper can be profitably used to identify both the type of classifier (e.g., single classifier or classifier ensemble) and how many and which noise levels should be used in the training phase in order to achieve the desired classification accuracy in the application domain of interest.