基于二十维组合特征的Naive Bayes Classifier预测金属离子配体的结合残基

蛋白质与金属离子配体的相互作用在生命进程中扮演着非常重要的角色.预测金属离子配体的结合残基对于理解细胞机制和设计分子药物有重要意义.文中使用Naive Bayes Classifier对十种金属离子配体Zn^(2+)、Fe^(2+)、Fe^(3+)、Cu^(2+)、Mn^(2+)、Co^(2+)、Ca^(2+)、Mg^(2+)、Na^+和K^+的结合残基进行预测,五交叉检验下得到了较好的预测结果.

A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant

With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis(LDA),principal component analysis(PCA)and partial least square(PLS)analysis.Recently,a mixed hidden naive Bayesian model(MHNBM)is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring.Although the MHNBM is effective,it still has some shortcomings that need to be improved.For the MHNBM,the variables with greater correlation to other variables have greater weights,which can not guarantee greater weights are assigned to the more discriminating variables.In addition,the conditional P(x j|x j′,y=k)probability must be computed based on historical data.When the training data is scarce,the conditional probability between continuous variables tends to be uniformly distributed,which affects the performance of MHNBM.Here a novel feature weighted mixed naive Bayes model(FWMNBM)is developed to overcome the above shortcomings.For the FWMNBM,the variables that are more correlated to the class have greater weights,which makes the more discriminating variables contribute more to the model.At the same time,FWMNBM does not have to calculate the conditional probability between variables,thus it is less restricted by the number of training data samples.Compared with the MHNBM,the FWMNBM has better performance,and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant(ZTPP),China.

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.

基于模糊聚类和Naive Bayes方法的文本分类器

本文提出一种文本分类的新方法 ,该方法将模糊聚类与基于NaiveBayes的EM分类算法相结合 ,从而大大提高了EM分类算法的准确性 ,并解决了使用字符匹配引起的不完整性和不准确性问题。该方法首先给出每个类的一些关键词 ,并把这些关键词作为聚类中心进行聚类 。

基于特征多视图提升Naive Bayesian的Boosting改进算法

AdaBoost作为一种有效的集成学习方法,能够明显提高不稳定学习算法的分类正确率,但对稳定的Naive Bayesian分类算法的提升效果却不明显.为此,利用多种特征评估函数建立不同的特征视图,生成多个有差异的加权朴素贝叶斯(WNB)基分类器;尝试使用几种不同的方式将样本权重嵌入WNB基分类器的参数中,对WNB产生扰动,进一步增加基分类器的不稳定性.实验结果表明,对比AdaBoost所提算法,Boost MV-WNB能够明显提升WNB文本分类器的性能.

一种Bayes降水概率预报的最优子集算法

MOS预报最优子集模型,通过消除数值模式系统性误差,可最大程度地提高其预报技巧。为了建立Nave Bayes降水最优模型,利用2008 2011年T511数值预报产品和单站观测资料,对介休、运城、丰宁3个站Nave Bayes降水概率分级预报模型进行研究。通过设计恰当的适应度函数,提出了一种用遗传算法搜寻Nave Bayes模型最优子集的计算方案,得到了3个站的最优子集模型。结果表明:最优子集的拟合效果明显高于普通初始子集,能够显著提升数值模式在单站的预报技巧。最优子集模型主要通过降低数值模式空报率提高单站晴雨、小雨预报效果,通过小幅提高正确次数和降低空报次数改善对中雨预报效果。

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.

基于Bayes网的软件构件分类

对软件构件进行分类有助于人们开发高质量的软件。Naive-Bayes网在分类中已经得到成功的应用。但是Naive-Bayes网有一个基本假设:各特征节点要求条件独立。不幸的事,这在现实世界中很难成立。论文利用主成分分析的方法降低了各特征节点的相关性,扩展了Naive-Bayes网的应用范围,并将其用于对软件构件进行分类。实例分析表明新的Bayes分类网预测精度高于一般的Naive-Bayes网。

基于朴素Bayes组合的简易集成分类器

朴素Bayes分类器是一种简单有效的机器学习工具.本文用朴素Bayes分类器的原理推导出“朴素Bayes组合”公式,并构造相应的分类器.经过测试,该分类器有较好的分类性能和实用性,克服了朴素Bayes分类器精确度差的缺点,并且比其他分类器更加快速而不会显著丧失精确度.

基于双信号融合的主轴/刀柄结合面刚度退化程度预测

为了预测主轴/刀柄结合面刚度退化程度,提出了一种基于激励和响应信号融合的主轴/刀柄结合面刚度退化程度预测方法。首先进行钛合金矩形工件侧铣实验,采集瞬时铣削力信号和主轴/刀柄结合面附近的响应振动信号,构建反映主轴/刀柄结合面刚度退化的数据库。然后根据数据库中瞬时铣削力和振动信号各方向的时域、频域和时频域特征,基于相关性分析优选出瞬时铣削力信号和振动信号的时域均值、频域中心频率、时频域一阶小波包能量3个特征,分别使用低频滤波卷积核和高频滤波卷积核对优选后的特征矩阵进行双通道卷积池化处理,获取深度融合的主轴/刀柄结合面刚度退化程度特征向量。最后以支持向量机模型(SVM)的概率模式转化为朴素贝叶斯分类器(NBC)的条件概率,构建混合分类器模型(NBC-SVM),提高了分类器的分类性能。在主轴/刀柄结合面刚度退化数据库的基础上,基于双通道卷积池化的特征融合方法(CP-FF)和NBC-SVM模型实现了主轴/刀柄结合面刚度退化程度的预测,预测精度达96%。

基于微震监测和概率优化朴素贝叶斯的短期岩爆预测模型

岩爆是地下岩土工程中常见的地压灾害。为实时准确地预测岩爆,提出一种基于微震监测和概率优化朴素贝叶斯的短期岩爆预测模型。首先,以114组岩爆样本数据为基础,结合相关特征选择算法选取累计微震事件数、累积微震能量、累积微震视体积和累积微震能量率4项微震参数作为预测指标。其次,为最大程度地削弱朴素贝叶斯算法的条件独立性假设,采用指标相关重要性赋权法和相似度函数从属性赋权和实例赋权两方面优化条件概率,并针对条件概率赋权后可能引起的决策失衡问题,引入马氏距离补偿先验概率损失,进而提出一种带有条件概率加权和先验概率补偿机制的概率优化朴素贝叶斯算法预测岩爆烈度等级。最后,从模型评估、模型比较和工程验证3个方面检验模型的准确性和可靠性。研究结果表明,所提模型预测准确率为86.96%,预测性能优于其他机器学习模型,可为实际工程的岩爆预测提供科学依据。

A Naive Bayes model on lung adenocarcinoma projection based on tumor microenvironment and weighted gene coexpression network analysis

Based on the lung adenocarcinoma(LUAD)gene expression data from the cancer genome atlas(TCGA)database,the Stromal score,Immune score and Estimate score in tumor microenvironment(TME)were computed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data(ESTIMATE)algorithm.And gene modules significantly related to the three scores were identified by weighted gene coexpression network analysis(WGCNA).Based on the correlation coefficients and P values,899 key genes affecting tumor microenvironment were obtained by selecting the two most correlated modules.It was suggested through Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis that these key genes were significantly involved in immune-related or cancer-related terms.Through univariate cox regression and elastic network analysis,genes associated with prognosis of the LUAD patients were screened out and their prognostic values were further verified by the survival analysis and the University of ALabama at Birmingham CANcer(UALCAN)database.The results indicated that eight genes were significantly related to the overall survival of LUAD.Among them,six genes were found differentially expressed between tumor and control samples.And immune infiltration analysis further verified that all the six genes were significantly related to tumor purity and immune cells.Therefore,these genes were used eventually for constructing a Naive Bayes projection model of LUAD.The model was verified by the receiver operating characteristic(ROC)curve where the area under curve(AUC)reached 92.03%,which suggested that the model could discriminate the tumor samples from the normal accurately.Our study provided an effective model for LUAD projection which improved the clinical diagnosis and cure of LUAD.The result also confirmed that the six genes in the model construction could be the potential prognostic biomarkers of LUAD.

Improving naive Bayes classifier by dividing its decision regions

Classification can be regarded as dividing the data space into decision regions separated by decision boundaries.In this paper we analyze decision tree algorithms and the NBTree algorithm from this perspective.Thus,a decision tree can be regarded as a classifier tree,in which each classifier on a non-root node is trained in decision regions of the classifier on the parent node.Meanwhile,the NBTree algorithm,which generates a classifier tree with the C4.5 algorithm and the naive Bayes classifier as the root and leaf classifiers respectively,can also be regarded as training naive Bayes classifiers in decision regions of the C4.5 algorithm.We propose a second division (SD) algorithm and three soft second division (SD-soft) algorithms to train classifiers in decision regions of the naive Bayes classifier.These four novel algorithms all generate two-level classifier trees with the naive Bayes classifier as root classifiers.The SD and three SD-soft algorithms can make good use of both the information contained in instances near decision boundaries,and those that may be ignored by the naive Bayes classifier.Finally,we conduct experiments on 30 data sets from the UC Irvine (UCI) repository.Experiment results show that the SD algorithm can obtain better generali-zation abilities than the NBTree and the averaged one-dependence estimators (AODE) algorithms when using the C4.5 algorithm and support vector machine (SVM) as leaf classifiers.Further experiments indicate that our three SD-soft algorithms can achieve better generalization abilities than the SD algorithm when argument values are selected appropriately.

Naive Bayes Classifier for Debris Flow Disaster Mitigation in Mount Merapi Volcanic Rivers,Indonesia,Using X-band Polarimetric Radar

Debris flow triggered by rainfall that accompanies a volcanic eruption is a serious secondary impact of a volcanic disaster.The probability of debris flow events can be estimated based on the prior information of rainfall from historical and geomorphological data that are presumed to relate to debris flow occurrence.In this study,a debris flow disaster warning system was developed by applying the Na?¨ve Bayes Classifier(NBC).The spatial likelihood of the hazard is evaluated at a small subbasin scale by including high-resolution rainfall measurements from X-band polarimetric weather radar,a topographic factor,and soil type as predictors.The study was conducted in the Gendol River Basin of Mount Merapi,one of the most active volcanoes in Indonesia.Rainfall and debris flow occurrence data were collected for the upper Gendol River from October 2016 to February 2018 and divided into calibration and validation datasets.The NBC was used to estimate the status of debris flow incidences displayed in the susceptibility map that is based on the posterior probability from the predictors.The system verification was performed by quantitative dichotomous quality indices along with a contingency table.Using the validation datasets,the advantage of the NBC for estimating debris flow occurrence is confirmed.This work contributes to existing knowledge on estimating debris flow susceptibility through the data mining approach.Despite the existence of predictive uncertainty,the presented system could contribute to the improvement of debris flow countermeasures in volcanic regions.

最大散度差分类器及其在文本分类中的应用

提出的最大散度差分类器是在修正Fisher线性鉴别准则的基础上建立起来的,它与Rocchio和SVM分类器有着十分密切的联系。在国际标准语料库20Newsgroups上进行的仿真实验结果表明,最大散度差分类器具有良好的文本分类性能,其正确识别率明显高于NaiveBayes和Rocchio,与SVM相当。

基于改进属性加权的朴素贝叶斯入侵取证研究

针对传统朴素贝叶斯分类模型在入侵取证中存在的特征项冗余问题,以及没有考虑入侵行为所涉及的数据属性间的差别问题,提出一种基于改进的属性加权朴素贝叶斯分类方法。用一种改进的基于特征冗余度的信息增益算法对特征项集进行优化,并在此优化结果的基础上,提取出其中的特征冗余度判别函数作为权值引入贝叶斯分类算法中,对不同的条件属性赋予不同的权值。经实验验证,该算法能有效地选择特征向量,降低分类干扰,提高检测精度。

基于词典属性特征的粗粒度词义消歧

本文依据《现代汉语语法信息词典》中对词语多义的属性特征描述,对《人民日报》语料中155个词语共4996个同形实例进行了粗粒度词义自动消歧实验,同时用贝叶斯算法进行了比较测试。基于词典属性特征的消歧方法在同形层面上准确率达到90%,但召回率偏低。其优点在于两个方面:1)不受词义标注语料库规模的影响;2)对特定词语意义的消歧准确率可达到100%。本文也讨论了适用于不同词类的消歧特征。

基于特征加权的朴素贝叶斯流量分类方法研究

研究了被广泛应用于互联网流量分类的朴素贝叶斯分类方法的性能特点,针对此方法在给定类别下给出的所有流量特征同等重要并且是独立的假设在现实中难以满足,致使分类准确率不高的问题,提出一种基于特征加权的朴素贝叶斯流量分类算法。该算法基于NetFlow记录的特征信息,采用特征选择算法ReliefF和相关系数方法计算每个特征的权重值,然后将网络流量分配至后验概率最大的应用类别中。实验结果表明,这种基于特征加权的朴素贝叶斯算法具有超过94%的分类准确率,并且维持了朴素贝叶斯方法简单高效、分类稳定的特性,可以满足当前高带宽网络流量分类的需求。

一种选择性的加权朴素贝叶斯分类器

朴素贝叶斯分类器是一种简单而高效的分类器,但它的条件独立性假设影响了它分类的正确率.加权朴素贝叶斯是对它的一种扩展.通过分析属性相关性的度量和属性约简,选择一组最近似独立的属性约简子集,并结合加权朴素贝叶斯和选择性贝叶斯分类器的优点,提出一种选择性的加权贝叶斯分类器SWNBC.实验结果表明,与朴素贝叶斯分类器相比,WSANBC分类器具有较高的分类正确率.

基于改进VGG-16和朴素贝叶斯的手写数字识别

为了解决手写数字识别困难和准确率问题,提出基于改进VGG-16和朴素贝叶斯的手写数字识别,主要通过归一化和双线性插值对图像进行预处理,然后通过改进的VGG-16网络框架对图像进行特征提取和特征融合,通过LDA方法进行数据降维,最后通过朴素贝叶斯分类器进行分类。在MNIST数据集中进行实验,获得了99.36%的准确率。实验结果验证了卷积神经网络与朴素贝叶斯结合后可以有效地提高识别准确率。