决策树与人工神经网络的对比分析

决策树和人工神经网络是数据挖掘分类任务中两项重要技术,各具特点,对不同的数据类型应采用不同的算法进行相应的研究应用。为了深入地说明各自的特点,根据决策树C 4.5算法的原理和流程,以及人工神经网络的BP网络模型原理和实现分类的流程,并应用具体的实例,对两种技术进行了对比分析研究,得出并验证了它们在实现分类中的一些性能差异。     

C4.5算法在保险业可疑交易分析中的应用

上报可疑交易报告是保险公司日常工作之一,目前对可疑交易的识别基本上是工作人员依照中国人民银行公布的《金融机构大额交易和可疑交易报告管理办法》对交易逐条进行识别。本文利用经典的决策树C4.5算法对交易进行分析,自动识别出部分可疑交易。     

分类挖掘在大学生智能评估系统中的设计与实现

主要介绍了耗时短、效率高、发展比较成熟的决策树算法C4．5,以及该算法在大学生智能评估系统中的分类挖掘子模块中的设计和实现。     

决策树算法在智能导学评价子系统中的应用

介绍智能导学系统的特点,并对决策树C4.5算法的原理进行了阐述,通过C4.5构造了一个学生在线学习效果的评估模型.并利用该模型得到的分类规则进行预测,得到准确性评估表,从而验证决策树算法的灵活性和计算的高效性.     

C4.5算法在保险客户流失分析中的应用

保持客户和吸引客户是保险公司提高竞争力的关键,目前保险公司对客户流失的分析是粗略的或根据经验来判断。论文利用面向属性归纳和决策树C4.5算法对保险客户基本信息进行分析,找出客户流失的特征,帮助保险公司有针对性地改善客户关系。     

Parallel Formulations of Decision-Tree Classification Algorithms

Classification decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, etc. Highly parallel algorithms for constructing classification decision trees are desirable for dealing with large data sets in reasonable amount of time. Algorithms for building classification decision trees have a natural concurrency, but are difficult to parallelize due to the inherent dynamic nature of the computation. In this paper, we present parallel formulations of classification decision tree learning algorithm based on induction. We describe two basic parallel formulations. One is based on Synchronous Tree Construction Approach and the other is based on Partitioned Tree Construction Approach. We discuss the advantages and disadvantages of using these methods and propose a hybrid method that employs the good features of these methods. We also provide the analysis of the cost of computation and communication of the proposed hybrid method. Moreover, experimental results on an IBM SP-2 demonstrate excellent speedups and scalability.     

An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging,Boosting, and Randomization

Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a base learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative approach to generating an ensemble is to randomize the internal decisions made by the base algorithm. This general approach has been studied previously by Ali and Pazzani and by Dietterich and Kong. This paper compares the effectiveness of randomization, bagging, and boosting for improving the performance of the decision-tree algorithm C4.5. The experiments show that in situations with little or no classification noise, randomization is competitive with (and perhaps slightly superior to) bagging but not as accurate as boosting. In situations with substantial classification noise, bagging is much better than boosting, and sometimes better than randomization.     

Using Model Trees for Classification

Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5, based on Quinlan's M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.     

基于并行C4.5算法的民机质量数据决策指导

民机设备系统每天都在产生大量的质量数据信息。随着时间的推移及数据量的积累,传统离散制造业的数据统计分析已经无法对这些庞大的质量数据进行有效地处理分析。为了解决这一问题,并挖掘出数据之间的隐含规律,提出了一种有效的数据挖掘方法。该方法通过集成决策树C4.5并行算法完成质量数据分析。分析结果展示了该分析方法的正确性、有效性和价值性。     

基于并行C4.5的铁路零散白货客户流失预测研究

为了提高铁路零散白货客户流失预测的准确性和高效性,根据铁路零散白货客户的流失特征,提出了基于CDL模型的客户流失识别方法,在此基础上,针对数据量大的问题,提出了基于Hadoop并行框架的C4.5决策树客户流失预测模型。通过仿真实验,证明该模型具有较好的准确性和预测能力,并且随着样本数量的增加,Hadoop并行框架的效率得到了明显的提升,且不影响客户流失预测模型的准确性和预测能力。     

基于改进的C4.5算法的网络流量分类方法

在基于C4.5算法的网络流量分类方法中,网络流量数据量的海量性及其特征的多样性使得决策树的构建速度、分类速度成为评价网络流量分类器的重要标准。在原C4.5算法的基础上提出一种改进的信息熵的计算方法,通过减少计算函数的复杂度,提高决策树的构建速度。实验表明,基于改进后算法的分类器在达到原有分类准确率的同时,极大地缩短了决策树的构成时间。     

Credal-C4.5: Decision tree based on imprecise probabilities to classify noisy data

In the area of classification, C4.5 is a known algorithm widely used to design decision trees. In this algorithm, a pruning process is carried out to solve the problem of the over-fitting. A modification of C4.5, called Credal-C4.5, is presented in this paper. This new procedure uses a mathematical theory based on imprecise probabilities, and uncertainty measures. In this way, Credal-C4.5 estimates the probabilities of the features and the class variable by using imprecise probabilities. Besides it uses a new split criterion, called Imprecise Information Gain Ratio, applying uncertainty measures on convex sets of probability distributions (credal sets). In this manner, Credal-C4.5 builds trees for solving classification problems assuming that the training set is not fully reliable. We carried out several experimental studies comparing this new procedure with other ones and we obtain the following principal conclusion: in domains of class noise, Credal-C4.5 obtains smaller trees and better performance than classic C4.5.     

一种基于变精度粗糙集的C45决策树改进算法*

针对C4.5决策树构造复杂、分类精度不高等问题,提出了一种基于变精度粗糙集的决策树构造改进算法.该算法采用近似分类质量作为节点选择属性的启发函数,与信息增益率相比,该标准更能准确地刻画属性分类的综合贡献能力,同时对噪声有一定的抑制能力.此外还针对两个或两个以上属性的近似分类质量相等的特殊情形,给出了如何选择最优的分类属...     

数据开采算法与数据库管理系统的接口设计

对大型数据库进行数据开采时,数据抽取问题及数据库和开采算法的接口设计就变得十分重要,通过定义SQL数据开采抽取器,设计了数据开采算法和数据库管理系统接口的框架体系,并通过常用的数据开采算法C4.5说明了这种标准的SQL数据开采抽取器的适用性。     

C4.5算法在列车轨道故障检测上的应用研究

列车轨道故障检测的实现需要对大量的数据进行分析来判定检测结果,决策树是进行数据挖掘与分类分析的常用工具。文中主要讨论如何应用C4.5算法构造列车轨道故障检测的决策树以及根据生成的决策树实现轨道故障的判决。