淮北煤田地下水微量元素贝叶斯多类线性判别分析

在淮北煤田主要含水层水中微量元素及常规离子测试分析的基础上,建立了微量元素Bayes多类线性判别模型。分析结果表明,在以As、Ba、Co、Cr、Cu、Se、V等7种特征微量元素为解释变量的Bayes多类线性判别函数中,Co与Cu对水源判别所起的作用较大,Ba最小。Ca2+、Mg2+、K++Na+、Cl-、SO42-、HCO3-等7种常规离子在判别函数中的系数绝对值相差不大。没有发现判别效果较强的常规离子,但经常规离子修正的微量元素Bayes多类线性判别模型具有很好的判别效果。

贝叶斯聚类在基因表达谱知识挖掘中的应用

在大规模基因表达谱的数据分析中引入了一种全新的基于贝叶斯模型的聚类算法。从生物学背景出发 ,研究了该算法应用在大规模基因表达谱中的理论基础和算法优越性 ,并应用该算法对两个公共的基因表达数据集进行了知识再挖掘。结果表明 ,与其他聚类算法相比 ,该算法在知识发现方面具有显著的优越性。

类属数据的贝叶斯聚类算法

针对类属型数据聚类中对象间距离函数定义的困难问题,提出一种基于贝叶斯概率估计的类属数据聚类算法。首先,提出一种属性加权的概率模型,在这个模型中每个类属属性被赋予一个反映其重要性的权重;其次,经过贝叶斯公式的变换,定义了基于最大似然估计的聚类优化目标函数,并提出了一种基于划分的聚类算法,该算法不再依赖于对象间的距离,而是根据对象与数据集划分间的加权似然进行聚类;第三,推导了计算属性权重的表达式,得出了类属型属性权重与其符号分布的信息熵成反比的结论。在实际数据和合成数据集上进行了实验,结果表明,与基于距离的现有聚类算法相比,所提算法提高了聚类精度,特别是在生物信息学数据上取得了5%~48%的提升幅度,并可以获得有实际意义的属性加权结果。

基于贝叶斯聚类的高速列车横向稳定性识别方法及其实时在线监测装置

针对国外高速列车横向失稳检测装置存在的误报和原始数据分析支持度不足的问题,根据车辆横向振动信号表现出的高斯统计特性和大量的现场试验数据,建立描述高速列车车辆横向加速度信号分布的高速列车高斯混合振动模型,运用期望值最大算法(EM算法)进行模型参数的估计,进而基于贝叶斯聚类算法实现高速列车横向稳定性的识别。运用给出的高速列车横向稳定性识别方法,设计由数据采集与处理模块、综合诊断模块、网络传输模块和车载实时数据库模块组成的高速列车横向稳定性实时在线监测装置。型式试验结果表明,该装置能够实现列车横向稳定性的识别,而且比既有设备的识别准确性更高。

SVM在中文广告分类中的应用

由于中文文法的特殊性,传统的文本分类算法不适用于中文文本的处理。结合类贝叶斯网络和SVM分类算法设计中文广告分类系统,有利于在实际应用中更精确地对广告特征进行分析。

基于超像素及贝叶斯合并的图像分割算法

针对超像素分割算法中普遍存在的过分割问题,结合Mean Shift算法和非参数贝叶斯聚类模型,提出了一种新的图像分割算法MS-BRM(Mean Shift based Bayesian Region Merging)。首先,利用Mean Shift算法对图像进行超像素分割,然后根据非参数贝叶斯聚类模型,融合超像素的空间信息,提出一种区域合并策略对超像素进行合并,得到了最终的分割结果。实验结果表明,MS-BRM算法改善了超像素的过分割问题,对图像进行分割的结果保留了图像的边界信息,更加符合人类视觉的判断结果。

一种用户指导的关系数据分类算法

提出了一种用户指导的关系数据分类算法。对朴素贝叶斯分类算法进行拓展,使其应用到多关系数据分类领域,并引入了用户指导的概念。该算法有效的提高了分类精确度,并且能够直接支持关系数据库,运行时间远远小于基于ILP技术的关系数据分类算法。

基于贝叶斯的大数据异常值检测模型研究

随着物联网、云计算、移动互联网等信息产业的不断进步,数据规模越来越大、处理难度也逐渐加大。而海量的数据中,异常数据会干扰后续的挖掘、预测与分析。因此,有必要对异常数据进行检测,提高数据的准确性。但由于数据量过大,大数据常常不具备明显的分布特征,然而对非典型特征的数据进行建模是一个挑战,因为推理会变得更加困难。以空气质量大数据为例,研究了如何将不具备明显分布特征的大数据,利用独立的高斯分布混合分布描述此类数据。在前期研究的高斯混合模型+神经网络的基础上优化了均值和协方差的选取,使得结果更加准确。这对于异常数据检测具有重要意义,更大程度地提高检测的准确性。

塔里木盆地顺北超深层碳酸盐岩输导体系三维地震属性多尺度表征及连通性分析

塔里木盆地顺北地区7000 m超深层广泛发育碳酸盐岩断控岩溶储层,其复杂的溶洞-裂缝系统为油气提供了良好的运移通道和储集空间.本文分尺度精细刻画断控岩溶输导体系三维内部结构.首先采用断裂增强滤波技术,提高超深层地震数据信噪比.其次,根据断控岩溶输导结构的地震反射特征,将其划分为大、中、小三种不同尺度结构模式.大尺度结构包括大型走滑断裂、溶洞及伴随的岩溶塌陷,具有规则的断裂强反射和“串珠状”反射的震相特征;中尺度结构主要为走滑断裂附近横向发育的次生断层及伴随的岩溶局部塌陷,具有杂乱强反射震相特征;小尺度结构为弥漫整个输导介质的背景裂缝,具有杂乱弱反射震相特征.单一的地震属性很难全面地描述断控岩溶输导介质,通过属性分析,明确了三类结构模式的敏感地震属性群,利用贝叶斯聚类融合技术,分别对三类结构模式的敏感地震属性群进行融合.最后,将三类结构模式的融合地震属性分别采样到三维输导介质网格中进行量化分析,最终建立缝洞连通体结构模型,为后续的岩性物性预测和流体识别提供三维输导体系连通体模型.

Agricultural Product Quality Mining Based on Bayesian Classification

Combined with the characteristics of crop growth and environmental data and the basic principle of Bayesian algorithm,the crop product quality is analyzed and forecasted in this study.Test with a randomly selected sample group ensures high forecasting accuracy,which shows that the algorithm is effective.

一个贝叶斯聚类方法及应用

聚类分析是研究对样品或指标进行综合分类的一种多元统计分析方法.聚类结果常表现为树状图的形式.如何合理确定聚类的个数,一直是一个比较困难的问题,至今没有很好的解决方案,尤其当样本量较大时,树状图层次较多,很难直观确定聚类个数.介绍一种基于贝叶斯理论的聚类方法,通过对后验似然最大化的原则确定最佳聚类个数和方案,避免了聚类个数选择的主观性.一个已知分类情况的实际数据验证了该聚类方法的有效性.

Retrieving reuse component based on semantic

According to the current research status of component retrieval, the component description model based on facet classification is improved by adding semantic features. Furthermore, the component retrieval process model is put forward by combining the domain ontology with the relative concept match algorithm. A detailed illustration of a component reasoning engine and a component classification engine is given and the component classification algorithm is provided by using the Naive Bayes algorithm based on domain ontology. The experimental results show that the recall ratio and the precision ratio are obviously improved by using the method based on semantics, and demonstrate the feasibility and effectiveness of the proposed method.

Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine

Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples according to their operating points. Then, the sub-models are estimated by Gaussian Process Regression （GPR）. Finally, in order to get a global probabilistic prediction, Bayesian committee mactnne is used to combine the outputs of the sub-estimators. The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators. Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.

Parallel naive Bayes algorithm for large-scale Chinese text classification based on spark

The sharp increase of the amount of Internet Chinese text data has significantly prolonged the processing time of classification on these data.In order to solve this problem,this paper proposes and implements a parallel naive Bayes algorithm(PNBA)for Chinese text classification based on Spark,a parallel memory computing platform for big data.This algorithm has implemented parallel operation throughout the entire training and prediction process of naive Bayes classifier mainly by adopting the programming model of resilient distributed datasets(RDD).For comparison,a PNBA based on Hadoop is also implemented.The test results show that in the same computing environment and for the same text sets,the Spark PNBA is obviously superior to the Hadoop PNBA in terms of key indicators such as speedup ratio and scalability.Therefore,Spark-based parallel algorithms can better meet the requirement of large-scale Chinese text data mining.

Automatically Constructing an Effective Domain Ontology for Document Classification

An effective domain ontology automatically constructed is proposed in this paper. The main concept is using the Formal Concept Analysis to automatically establish domain ontology. Finally, the ontology is acted as the base for the Naive Bayes classifier to approve the effectiveness of the domain ontology for document classification. The 1752 documents divided into 10 categories are used to assess the effectiveness of the ontology, where 1252 and 500 documents are the training and testing documents, respectively. The Fl-measure is as the assessment criteria and the following three results are obtained. The average recall of Naive Bayes classifier is 0.94. Therefore, in recall, the performance of Naive Bayes classifier is excellent based on the automatically constructed ontology. The average precision of Naive Bayes classifier is 0.81. Therefore, in precision, the performance of Naive Bayes classifier is gored based on the automatically constructed ontology. The average Fl-measure for 10 categories by Naive Bayes classifier is 0.86. Therefore, the performance of Naive Bayes classifier is effective based on the automatically constructed ontology in the point of F 1-measure. Thus, the domain ontology automatically constructed could indeed be acted as the document categories to reach the effectiveness for document classification.

Social Network Information Propagation Model Based on Individual Behavior

In this paper, we discuss building an information dissemination model based on individual behavior. We analyze the individual behavior related to information dissemination and the factors that affect the sharing behavior of individuals, and we define and quantify these factors. We consider these factors as characteristic attributes and use a Bayesian classifier to classify individuals. Considering the forwarding delay characteristics of information dissemination, we present a random time generation method that simulates the delay of information dissemination. Given time and other constraints, a user might not look at all the information that his/her friends published. Therefore, this paper proposes an algorithm to predict information visibility, i.e., it estimates the probability that an individual will see the information. Based on the classification of individual behavior and combined with our random time generation and information visibility prediction method, we propose an information dissemination model based on individual behavior. The model can be used to predict the scale and speed of information propagation. We use data sets from Sina Weibo to validate and analyze the prediction methods of the individual behavior and information dissemination model based on individual behavior. A previously proposedinformation dissemination model provides the foundation for a subsequent study on the evolution of the network and social network analysis. Predicting the scale and speed of information dissemination can also be used for public opinion monitoring.

利用微卫星标记分析中国地方绵羊品种的遗传多样性

试验选取19个微卫星标记对中国10个地方绵羊品种和1个引进品种的遗传多样性和系统进化关系进行分析。结果表明,10个中国地方绵羊品种的平均等位基因数在5.44(贵德黑裘皮羊)～9.13(乌珠穆沁羊和呼伦贝尔羊)之间,期望杂合度在0.623(贵德黑裘皮羊)～0.737(昭通绵羊)之间,基因丰富度在5.169(贵德黑裘皮羊)～7.610(昭通绵羊)之间。19个标记中有3个微卫星位点(SRCRSP5、OarAE129和DYMS1)显著偏离Hardy-Wein-berg平衡(P<0.05)。中国地方绵羊品种中有95.23%的遗传变异存在于品种内,而4.77%的遗传变异存在于品种间。中国地方绵羊品种间的遗传变异模式和遗传关系与传统的分类相一致。贝叶斯聚类分析和主成分分析结果表明,蒙古羊、哈萨克羊、藏绵羊位于3个主分支,其次是昭通绵羊和贵德黑裘皮羊,这可能与其育种史不同、近交水平不同及地理隔离有关。本研究结果揭示了中国地方绵羊品种的遗传学特性,为今后地方品种资源的保存利用奠定基础。

FLBS: Fuzzy lion Bayes system for intrusion detection in wireless communication network

An important problem in wireless communication networks (WCNs) is that they have a minimum number of resources, which leads to high-security threats. An approach to find and detect the attacks is the intrusion detection system (IDS). In this paper, the fuzzy lion Bayes system (FLBS) is proposed for intrusion detection mechanism. Initially, the data set is grouped into a number of clusters by the fuzzy clustering algorithm. Here, the Naive Bayes classifier is integrated with the lion optimization algorithm and the new lion naive Bayes (LNB) is created for optimally generating the probability measures. Then, the LNB model is applied to each data group, and the aggregated data is generated. After generating the aggregated data, the LNB model is applied to the aggregated data, and the abnormal nodes are identified based on the posterior probability function. The performance of the proposed FLBS system is evaluated using the KDD Cup 99 data and the comparative analysis is performed by the existing methods for the evaluation metrics accuracy and false acceptance rate (FAR). From the experimental results, it can be shown that the proposed system has the maximum performance, which shows the effectiveness of the proposed system in the intrusion detection.

Comparison of six statistical approaches in the selection of appropriate fish growth models

The performance of six statistical approaches,which can be used for selection of the best model to describe the growth of individual fish,was analyzed using simulated and real length-at-age data.The six approaches include coefficient of determination(R2),adjusted coefficient of determination(adj.-R2),root mean squared error(RMSE),Akaike's information criterion(AIC),bias correction of AIC(AICc) and Bayesian information criterion(BIC).The simulation data were generated by five growth models with different numbers of parameters.Four sets of real data were taken from the literature.The parameters in each of the five growth models were estimated using the maximum likelihood method under the assumption of the additive error structure for the data.The best supported model by the data was identified using each of the six approaches.The results show that R2 and RMSE have the same properties and perform worst.The sample size has an effect on the performance of adj.-R2,AIC,AICc and BIC.Adj.-R2 does better in small samples than in large samples.AIC is not suitable to use in small samples and tends to select more complex model when the sample size becomes large.AICc and BIC have best performance in small and large sample cases,respectively.Use of AICc or BIC is recommended for selection of fish growth model according to the size of the length-at-age data.

Liver Hydatid CT Image Segmentation Using Smoothed Bayesian Classification Method and Modified Parametric Active Contour Model

Liver hydatid disease is a common parasitic disease in farm and pastoral areas, which seriously influences people's health. Based on CT imaging features of this disease, an iterative approach for liver segmentation and hydatid lesion extraction simultaneously is proposed. In each iteration, our algorithm consists of two main steps: 1) according to the user-defined pixel seeds in the liver and hydatid lesion, Gaussian probability model fitting and smoothed Bayesian classification are applied to get initial segmentation of liver and lesion; 2) the parametric active contour model using priori shape force field is adopted to refine initial segmentation. We make subjective and objective evaluation on the proposed algorithm validity by the experiments of liver and hydatid lesion segmentation on different patients' CT slices. In comparison with ground-truth manual segmentation results, the experimental results show the effectiveness of our method to segment liver and hydatid lesion.