遗传算法在模糊模型参数辨识中的应用

介绍了T-S模糊模型的建模过程,在现有T-S模糊模型参数辨识方法的基础上,提出了一种先应用最小二乘法对结论参数进行粗略辨识,以确定参数的大致范围之后,再应用遗传算法对前提参数和结论参数同时优化的参数辨识方法,通过MATLAB对本算法进行了仿真,并对非线性函数进行了逼近实验,所取得的结果令人满意。     

基于朴素贝叶斯算法的中文评论分类

针对中文评论分类问题,采用朴素贝叶斯算法进行深入研究.首先,根据中文评论分类的需求设计了朴素贝叶斯分类器;然后,使用WEKA以不同特征提取方式对其功能性进行了对比分析.通过一系列的实验数据的横向对比表明,在朴素贝叶斯分类器下采用集成特征选取时文本分类的准确率最佳,准确率达97.65％,验证了朴素贝叶斯分类器在处理中文评...     

混合树增广朴素贝叶斯分类模型

树增广朴素贝叶斯分类算法(TANC)虽然降低了朴素贝叶斯分类算法(NBC)的条件独立性约束,但是该模型同时又要求每个条件属性结点(除树的根结点外)都有两个父结点,这种限制同样降低了分类的正确率.因此,提出了一种基于粗糙集理论的混合树增广朴素贝叶斯分类模型(MTANC).通过在UCI数据集上的仿真实验,验证了该方法的有效性.     

Constructing a novel mortality prediction model with Bayes theorem and genetic algorithm

Intensive care is one of the most important components of the modern medical system. Healthcare professionals need to utilize intensive care resources effectively. Mortality prediction models help physicians decide which patients require intensive care the most and which do not. The Simplified Acute Physiology System 2nd version (SAPS II) is one of the most popular mortality scoring systems currently available. This study retrospectively collected data on 496 patients admitted to intensive care units from year 2000 to 2001. The average patient age was 59.96 ± 1.83 years old and 23.8% of patients died before discharge. We used these data as training data and constructed an exponential Bayesian mortality prediction model by combining BSM (Bayesian statistical model) and GA (genetic algorithm). The optimal weights and the parameters were determined with GA. Furthermore, we prospectively collected data on 142 patients for testing the new model. The average patient age for this group was 57.80 ± 3.33 years old and 21.8% patients died before discharge. The mortality prediction power of the new model was better than SAPS II (p < 0.001). The new model combining BSM and GA can manage both binary data and continuous data. The mortality rate is predicted to be high if the patient’s Glasgow coma score is less than 5.     

基于改进特征加权的朴素贝叶斯分类算法

传统朴素贝叶分类算法没有根据特征项的不同对其重要程度进行划分,使得分类结果不准确。针对这一问题,引入Jensen-Shannon（JS）散度,用JS散度来表示特征项所能提供的信息量,并针对JS散度存在的不足,从类别内与类别间的词频、文本频以及用变异系数修正过的逆类别频率这三个方面考虑,对JS散度进行调整修正,最后计算出每一特征项的权值,将权值带入到朴素贝叶斯的公式中。通过与其他算法的对比实验证明,基于JS散度并从词、文本、类别三方面改进后的朴素贝叶斯算法的分类效果最好。因此基于JS散度特征加权的朴素贝叶斯分类算法与其他分类算法相比,其分类性能有很大提高。     

朴素贝叶斯算法在垃圾邮件过滤中的分析和应用

朴素贝叶斯方法(Naive Bayes)以其运行快速、易于实现的特点,被广泛应用于各种文本分类和邮件过滤的应用系统中,但现有以NB为基础的过滤系统在分类性能、准确率等方面还存在一些问题,深入研究需要了解相关的背景知识.本文首先分析和比较了现存的各种NB版本,总结各个NB的优点和不足,进而又介绍和比较了具有代表性的各种NB改进算法,目的是便于研究者在进行改进和深入研究时能有一个明确的方向.     

贝叶斯方法在文本分类预处理中的应用

在文本分类预处理过程中,运用贝叶斯方法构造计算文本关键词的条件概率模型,通过计算文本关键词的出现概率将文本映射为关键词的概率向量。在这个过程中贝叶斯方法用于计算条件概率而非分类。     

分布式朴素贝叶斯算法在文本分类中的应用

传统串行贝叶斯算法在对大规模数据进行分类时,性能较低下.为此,在TFIDF(词频-逆向文件频率)特征加权基础上,提出ICF(逆类别因子)类别加权因子,对传统贝叶斯分类模型进行改进.利用MapReduce并行计算框架在处理海量数据方面的优势,设计并实现了一种对TFIDF改进的分布式朴素贝叶斯文本分类算法.实验结果表明,与传统分布式朴素贝叶斯算法和TFIDF加权的分布式朴素贝叶斯算法相比,改进后的分类算法在查准率、查全率、F-measure等方面都有了较大提高.     

一种基于BP和朴素贝叶斯的时间序列分类模型

针对传统时间序列分类方法需要较为繁琐的特征抽取工作以及在只有少量标记数据时分类效果不佳的问题,通过分析BP神经网络和朴素贝叶斯分类器的特点,提出一种基于BP和朴素贝叶斯的时间序列分类模型。利用了BP神经网络非线性映射能力和朴素贝叶斯分类器在少量标记数据下的分类能力,将BP神经网络抽取到的特征输入到朴素贝叶斯分类器中,可以较为有效的解决传统时间序列分类算法的问题。实验结果表明,该模型在标记数据较少的情况下的时间序列分类中具有较高的分类准确度。     

最大价值量Naive Bayes算法及在客户离网中的应用

针对不同级别不同数量的客户离网后给电信企业带来的损失不同造成的离网预测的新问题,提出了一种基于最大价值量的Naive Bayes算法.该算法通过建立价值量的概念,调整价值敏感属性的价值系数因子,使得离网客户名单中的价值量达到最大.实验结果表明,该算法在保持一定的准确率的同时,能成功预测出更多高价值的离网客户.     

模糊遗传算法在敏感词分类优化中的应用

针对以往敏感词分类优化的不足,提出一种基于模糊遗传算法的敏感词分类优化方法,该方法把模糊逻辑理论用于遗传算法,模拟生物进化过程和机制来求解实际的敏感词定性结构优化问题。研究表明,对于敏感词词性以及结构的变化有很好的分类优化效果,从而保证了整体的分类质量、快速的分类效率、鲁棒和可靠的分类性能。     

基于密度函数的高斯朴素贝叶斯集成算法研究

本文针对数据挖掘算法中的分类问题,针对连续性数据,提出了基于密度函数的高斯朴素贝叶斯集成算法.首先假设各特征值符合正态分布,计算出各特征值的均值和方差,也就是正态分布的密度函数.然后通过定义的密度函数,计算出其概率密度函数,利用高斯朴素贝叶斯分类器得到预测结果.在对某公司实际分类问题中应用该算法,结果表明该算法的预测能...     

基于条件信息熵的自主式朴素贝叶斯分类算法

朴素贝叶斯是一种简单而高效的分类算法,但其条件独立性和属性重要性相等的假设并不符合客观实际,这在某种程度上影响了它的分类性能。如何去除这种先验假设,根据数据本身的特点实现知识自主学习是机器学习中的一个难题。根据Rough Set的相关理论,提出了基于条件信息熵的自主式朴素贝叶斯分类方法,该方法结合了选择朴素贝叶斯和加权朴素贝叶斯的优点。通过在UCI数据集上的仿真实验,验证了该方法的有效性。     

基于多特征的Android恶意软件检测方法

传统的基于权限的Android恶意软件检测方法检测率较高,但存在较高的误报率,而基于函数调用的检测方法特征提取困难,难以应用到移动平台上。因此,在保留传统权限特征的基础上,提出了以权限和资源文件多特征组合方式的朴素贝叶斯检测方法,该方法所选特征提取简便,且具有较低的误报率,有效弥补传统检测方法的不足。实验从4 396个恶意样本和4 500个正常样本中随机抽取5组恶意样本和5组正常样本集,分别作了基于权限和基于多特征的对比实验。实验结果表明,与基于权限的分类方法相比,基于多特征的分类方法能显著地降低误报率,因此基于多特征的检测方法效果更优。     

Pollution source identification using a coupled diffusion model with a genetic algorithm

A new approach for the source quantification has been developed on the basis of real air pollutant hourly concentrations of SO₂, measured by three monitoring stations, during 9 h, around a group of three industrial sources. This inverse problem has been solved by coupling a direct model of diffusion (Pasquill’s Gaussian model) with a genetic algorithm, to search solutions leading to a minimum error between model outputs and measurements. The inversion performance depends on the relationship between the wind field and the configuration sources–receptors: good results are obtained when the monitoring stations are downwind from the sources, and in these cases, the order of magnitude of emissions is retrieved, sometimes with less than 10% error for at least two sources; there are some configurations (wind direction versus source and receptor locations) which do not permit to restore emissions. The latter situations reveal the need to conceive a specific network of sensors, taking into account the source locations and the most frequent weather patterns.     

Pollution source identification using a coupled diffusion model with a genetic algorithm

A new approach for the source quantification has been developed on the basis of real air pollutant hourly concentrations of SO₂, measured by three monitoring stations, during 9 h, around a group of three industrial sources. This inverse problem has been solved by coupling a direct model of diffusion (Pasquill’s Gaussian model) with a genetic algorithm, to search solutions leading to a minimum error between model outputs and measurements. The inversion performance depends on the relationship between the wind field and the configuration sources–receptors: good results are obtained when the monitoring stations are downwind from the sources, and in these cases, the order of magnitude of emissions is retrieved, sometimes with less than 10% error for at least two sources; there are some configurations (wind direction versus source and receptor locations) which do not permit to restore emissions. The latter situations reveal the need to conceive a specific network of sensors, taking into account the source locations and the most frequent weather patterns.     

朴素贝叶斯算法在反垃圾邮件系统中的应用

在进行文本信息的分类中,通过朴素贝叶斯算法对邮件进行分类是一种简单有效的方法,朴素贝叶斯在分类时假设属性之间条件独立,降低了复杂度。该文结合应用实例,给出了朴素贝叶斯算法在反垃圾邮件中的分类原理,达到了智能动态过滤垃圾邮件的效果。     

基于改进遗传算法的非线性系统模糊辨识新方法

提出一种基于改进遗传算法和递推最小二乘的非线性模糊辨识新算法.该辨识方法包含结构辨识辨出和参数辨识,结构辨识即输入空间的模糊划分,采用具有自适应性的广义高斯隶属函数;参数辨识包含前提参数和结论参数,用基于动态比例变换的改进遗传算法优化高斯函数的前提参数,用递推最小二乘辨识模糊模型的结论参数.最后通过著名的Box-Jenkins煤气炉数据仿真(仿真环境:MATLAB 6.5,计算机主频2.4 GHz,内存512 MB),并根据输入变量个数和模糊规则数,得到均方误差以证明本文方法的辨识精度,将该文辨识方法与其他方法进行比较,验证了该方法辨识精度更高.     

Attribute weighted Naive Bayes classifier using a local optimization

The Naive Bayes classifier is a popular classification technique for data mining and machine learning. It has been shown to be very effective on a variety of data classification problems. However, the strong assumption that all attributes are conditionally independent given the class is often violated in real-world applications. Numerous methods have been proposed in order to improve the performance of the Naive Bayes classifier by alleviating the attribute independence assumption. However, violation of the independence assumption can increase the expected error. Another alternative is assigning the weights for attributes. In this paper, we propose a novel attribute weighted Naive Bayes classifier by considering weights to the conditional probabilities. An objective function is modeled and taken into account, which is based on the structure of the Naive Bayes classifier and the attribute weights. The optimal weights are determined by a local optimization method using the quasisecant method. In the proposed approach, the Naive Bayes classifier is taken as a starting point. We report the results of numerical experiments on several real-world data sets in binary classification, which show the efficiency of the proposed method.     

A fuzzy GARCH model applied to stock market scenario using a genetic algorithm

In this paper, we derive a new application of fuzzy systems designed for a generalized autoregression conditional heteroscedasticity (GARCH) model. In general, stock market performance is time-varying and nonlinear, and exhibits properties of clustering. The latter means simply that certain large changes tend to follow other large changes, and in general small changes tend to follow other small changes. This paper shows results from using the method of functional fuzzy systems to analyze the clustering in the case of a GARCH model.The optimal parameters of the fuzzy membership functions and GARCH model are extracted using a genetic algorithm (GA). The GA method aims to achieve a global optimal solution with a fast convergence rate for this fuzzy GARCH model estimation problem. From the simulation results, we have determined that the performance is significantly improved if the leverage effect of clustering is considered in the GARCH model. The simulations use stock market data from the Taiwan weighted index (Taiwan) and the NASDAQ composite index (NASDAQ) to illustrate the performance of the proposed method.