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。     

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.     

基于K-近邻法的局部加权朴素贝叶斯分类算法

分类算法一直以来都是数据挖掘领域的研究重点,朴素贝叶斯分类算法是众多优秀分类算法之一,但由于其条件属性必需独立,使得该算法也存在着一定的局限性。为了从另外一种角度来改进该算法,提高分类性能,提出了一种基于K-近邻法的局部加权朴素贝叶斯分类算法。使用K-近邻法对属性加权,找到最合适的加权值,运用加权后的朴素贝叶斯分类算法去分类,实验表明该算法提高了分类的可靠性与准确率。     

基于遗传算法的朴素贝叶斯分类

朴素贝叶斯分类器是一种简单而高效的分类器,但是其属性独立性假设限制了对实际数据的应用。提出一种新的算法,该算法为避免数据预处理时,训练集的噪声及数据规模使属性约简的效果不太理想,并进而影响分类效果,在训练集上通过随机属性选取生成若干属性子集,并以这些子集构建相应的贝叶斯分类器,进而采用遗传算法进行优选。实验表明,与传统的朴素贝叶斯方法相比,该方法具有更好的分类精度。     

基于互信息的加权朴素贝叶斯文本分类算法

文本分类是信息检索和文本挖掘的重要基础,朴素贝叶斯是一种简单而高效的分类算法,可以应用于文本分类.但是其属性独立性和属性重要性相等的假设并不符合客观实际,这也影响了它的分类效果.如何克服这种假设,进一步提高其分类效果是朴素贝叶斯文本分类算法的一个难题.根据文本分类的特点,基于文本互信息的相关理论,提出了基于互信息的特征项加权朴素贝叶斯文本分类方法,该方法使用互信息对不同类别中的特征项进行分别赋权,部分消除了假设对分类效果的影响.通过在UCIKDD数据集上的仿真实验,验证了该方法的有效性.     

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。     

朴素贝叶斯分类中的隐私保护方法研究

数据挖掘中的隐私保护方法,试图在不精确访问原始数据详细信息的条件下,挖掘出准确的模式与规则.围绕着分类挖掘中的隐私保护问题展开研究,给出了一种基于数据处理和特征重构的朴素贝叶斯分类中的隐私保护方法.分别提出了一种针对枚举类型的隐私数据处理与特征重构方法--扩展的部分隐藏随机化回答(Extended Randomized Response with Partial Hiding,ERRPH)方法和一种针对数值类型的隐私数据处理与特征重构方法--转换的随机化回答(Transforming Randomized Response,TRR)方法,并在此基础上实现了一个完整的隐私保护的朴素贝叶斯分类算法.理论分析和实验结果均表明:朴素贝叶斯分类中基于ERRPH和TRR的隐私保护方法具有很好的隐私性、准确性、高效性和适用性.     

Numeric mapping and learnability of naive bayes

In data-mining applications, it is common to transform (or map) nominal attributes into numeric ones in order to apply a specific model. However, a nominal attribute has typically no specific order in its values and no geometric meaning. An interesting issue is, does such a transformation change the property of a nominal function? How do you measure the geometric complexity of a nominal function independent of the mapping? This paper discusses the issue of converting a nominal function into a numeric one. We propose a three-layer measure for the geometric linearity of a nominal function and explore the geometric property of a nominal function independent of the mapping. Naive Bayes is one of the most efficient and effective inductive-learning algorithms for data mining. It is well known that Naive Bayes is linear in the binary domain; that is, it can learn only linearly separable functions. We show that Naive Bayes is actually nonlinear in the nominal domain, a general case of the binary domain, by exploring the geometric property of Naive Bayes. We investigate the geometric property of Naive Bayes based on the three-layer linearity measure that we propose. Our work helps researchers to understand the influence of numeric mapping on the property of a nominal function, and how numeric mapping affects the learnability of Naive Bayes.     

基于实例和属性加权朴素贝叶斯的电气事故分类研究

为实现对电气事故快速、准确和动态的分类,提出一种有机结合实例和属性加权的朴素贝叶斯电气事故分类方法(AIWNB)。朴素贝叶斯分类方法中的先验概率和条件概率采用两种实例加权方式加以改进,积极实例权值取决于各属性值频度的统计值,而消极实例权值通过逐条计算训练实例与测试实例间的相关性加以确定。属性权值则基于互信息定义为属性-属性相关性和属性-类相关性之间的残差。所提出的AIWNB方法将属性加权和实例加权有机结合在朴素贝叶斯统一框架内,利用高低压用户的电气实测数据进行验证,实验结果表明,与朴素贝叶斯相比,加权后的朴素贝叶斯方法更具竞争性,准确率和F1分数可提升3.09%和9.39%,证明所提的AIWNB算法在电气事故分类的实用性及有效性,并可推广至其他分类情形。     

基于数据填补和连续属性的朴素贝叶斯算法

朴素贝叶斯算法（NB）在处理分类问题时通常假设训练样本的数值型连续属性满足正态分布,其分类精度也受到训练数据完整性的影响,而实际采样数据很难满足上述要求。针对数据缺失问题,基于期望最大值算法（EM）,将朴素贝叶斯分类器利用已有的不完整数据进行参数学习;针对样本数值型连续属性非正态分布的情况,基于核密度估计,利用其分布密度（Distribution Density）和新的分析计算方法来求最大后验分布,同时用标准数据集的分类实验验证了改进的有效性。将改良的算法EM-DNB应用在生物工程蛋白质纯化工艺预测中,实验结果表明,预测精度有所提高。     

A feature dependent Naive Bayes approach and its application to the software defect prediction problem

Naive Bayes is one of the most widely used algorithms in classification problems because of its simplicity, effectiveness, and robustness. It is suitable for many learning scenarios, such as image classification, fraud detection, web mining, and text classification. Naive Bayes is a probabilistic approach based on assumptions that features are independent of each other and that their weights are equally important. However, in practice, features may be interrelated. In that case, such assumptions may cause a dramatic decrease in performance. In this study, by following preprocessing steps, a Feature Dependent Naive Bayes (FDNB) classification method is proposed. Features are included for calculation as pairs to create dependence between one another. This method was applied to the software defect prediction problem and experiments were carried out using widely recognized NASA PROMISE data sets. The obtained results show that this new method is more successful than the standard Naive Bayes approach and that it has a competitive performance with other feature-weighting techniques. A further aim of this study is to demonstrate that to be reliable, a learning model must be constructed by using only training data, as otherwise misleading results arise from the use of the entire data set.     

层次式文本分类的Naive Bayes改进方法

Naive Bayes方法在文本分类中的决策强烈依赖于主观选择的样本关于类别的分布。本文利用层次式分类的特点并引入概率条件改进Naive Bayes方法,使其在每个内部类别所属的子类局部数据中进行决策,缓解了全局数据分布对分类器的影响,部分克服了数据偏斜问题。实验表明,改进方法在层次式分类中的效果较Naive Bayes方法有显著提高     

Kernel-based linear classification on categorical data

Kernel-based methods have been widely investigated in the soft-computing community. However, they focus mainly on numeric data. In this paper, we propose a novel method for kernel learning on categorical data, and show how the method can be used to derive effective classifiers for linear classification. Based on kernel density estimation for categorical attributes, three popular classification methods, i.e., Naive Bayes, nearest neighbor and prototype-based classification, are effectively extended to classify categorical data. We also propose two data-driven approaches to the bandwidth selection problem, with one aimed at minimizing the mean squared error of the kernel estimate and the other endeavored to attribute weights optimization. Theoretical analysis indicates that, as in the numeric case, kernel learning of categorical attributes is capable to make the classes to be more separable, resulting in outstanding performances of the new classifiers on various real-world data sets.     

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Rough Set的加权朴素贝叶斯分类算法

朴素贝叶斯算法是一种简单而高效的分类算法，但其条件独立性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。加权朴素贝叶斯是对它的一种扩展。基于Rough Set的属性重要性理论，提出了基于Rough Set的加权朴素贝叶斯分类方法，并分别从代数观、信息观及综合代数观和信息观的角度给出了属性权值的求解方法。通过在UCI数据集上的仿真实验，验证了该方法的有效性。     

在逐渐缩小的空间上渐进学习朴素贝叶斯参数

局部加权朴素贝叶斯(LWNB)是朴素贝叶斯(NB)的一种较好的改进,判别频率估计(DFE)可以极大地提高NB的泛化正确率。受LWNB和DFE启发,提出逐渐缩小空间(GCS)算法用来学习NB参数：对于一个测试实例,寻找包含全体训练实例的全局空间的一系列逐渐缩小的子空间。这些子空间具有两种性质：1)它们都包含测试实例;2)一个空间一定包含在任何一个比它大的空间中。在逐渐缩小的空间上使用修改的DFE(MDFE)算法渐进地学习NB的参数,然后使用NB分类测试实例。与LWNB的根本不同是：GCS使用全体训练实例学习NB并且GCS可以实现为非懒惰版本。实现了GCS的决策树版本(GCS-T),实验结果显示,与C4.5以及贝叶斯分类算法(如Naive Bayes、BaysianNet、NBTree、LWNB、隐朴素贝叶斯)相比,GCS-T具有较高的泛化正确率,并且GCS-T的分类速度明显快于LWNB。     

面向Stacking集成的改进分类算法及其应用

为了提高Stacking集成算法的分类性能,充分利用Stacking学习机制产生的先验信息和贝叶斯网络丰富的概率表达能力,提出一种基于属性值加权朴素贝叶斯算法的Stacking集成分类算法AVWNB-Stacking(Stacking based Attribute Value Weight Naive Bayes)。通过考虑属性值这个深层次的因素,以互信息(Mutual Information,MI)作为权值度量的基础,对属性权值向量横向扩展为每个属性值分配一个权值,避免不同的属性值共享相同的权值,从而解决朴素贝叶斯算法作为Stacking元分类器由于属性独立性假设带来的分类精度损失。实验结果表明,相比于传统算法及其他元分类器的Stacking分类算法,AVWNB-Stacking算法有效提高了模型的分类性能,在两个测试集上AUC值分别达到了0.8007和0.8607。     

Learning Instance Weighted Naive Bayes from labeled and unlabeled data

In real-world data mining applications, it is often the case that unlabeled instances are abundant, while available labeled instances are very limited. Thus, semi-supervised learning, which attempts to benefit from large amount of unlabeled data together with labeled data, has attracted much attention from researchers. In this paper, we propose a very fast and yet highly effective semi-supervised learning algorithm. We call our proposed algorithm Instance Weighted Naive Bayes (simply IWNB). IWNB firstly trains a naive Bayes using the labeled instances only. And the trained naive Bayes is used to estimate the class membership probabilities of the unlabeled instances. Then, the estimated class membership probabilities are used to label and weight unlabeled instances. At last, a naive Bayes is trained again using both the originally labeled data and the (newly labeled and weighted) unlabeled data. Our experimental results based on a large number of UCI data sets show that IWNB often improves the classification accuracy of original naive Bayes when available labeled data are very limited.     

基于Map Reduce的Bagging贝叶斯文本分类

集中式系统框架难以进行海量文本数据分类。为此,提出一种基于Map Reduce的Bagging贝叶斯文本分类算法。介绍朴素贝叶斯文本分类算法,将其与Bagging算法结合,运用Map Reduce并行编程模型,在Hadoop平台上实现算法。实验结果表明,该算法分类准确率较高,运行时间较短,适用于大规模文本数据集的分类学习。     

浅析贝叶斯方法在文本分类中的应用

贝叶斯分类算法是基于贝叶斯全概率公式的分类算法,是一种简单有效的分类方法.本文系统的介绍贝叶斯分类算法的原理及贝叶斯分类算法的特点,并重点阐述两种常用的贝叶斯分类算法模型及应用.