规则简化与模糊决策树剪枝的比较

决策树归纳学习算法是机器学习领域中解决分类问题的最有效工具之一。由于决策树算法自身的缺陷了，因此需要进行相应的简化来提高预测精度。模糊决策树算法是对决策树算法的一种改进，它更加接近人的思维方式。文章通过实验分析了模糊决策树、规则简化与模糊规则简化；模糊决策树与模糊预剪枝算法的异同，对决策树的大小、算法的训练准确率与测试准确率进行比较，分析了模糊决策树的性能，为改进该算法提供了一些有益的线索。     

模糊决策树中参数对模糊熵的敏感性分析

模糊决策树的ID3算法是Quinlan提出的传统ID3算法的一个模糊版本。树的整个产生过程在给定的显著性水平α的基础上进行，的值在很大程度上影响模糊熵的计算。从而影响模糊决策树最终的分类结果。对参数α关于模糊熵的敏感性进行了分析，试图定性地找出二者之间的解析关系，从而为选取参数α的值以达到最优的分类结果提供理论依据。     

自适应模糊决策树算法

为改善模糊决策树算法凭经验设定参数值的不准确问题,在分析模糊决策树算法的主要参数特征后,提出使用粒子群算法智能设定参数值的自适应模糊决策树算法.实验表明,与经验设定参数值的模糊决策树算法相比,自适应模糊决策树算法生成的模糊决策树的性能明显提高;最后,通过实验数据分析了关键参数之间存在的交互影响关系.     

用遗传算法构造决策树

Ｃ４．５是一种归纳学习算法，它通过对一组事例的学习形成决策树形式的规则。由于Ｃ４．５采用的是局部探索的策略，它得到的决策树不一定是最优的。遗传算法是模拟自然进化的通用全局搜索算法。文中讨论了利用遗传算法的构造决策树的方法。     

基于遗传算法的模糊决策树的参数优化

模糊决策树归纳学习是从示例中产生规则知识的一个重要方法,决策树的产生过程涉及到两个重要的参数α、β。一般说来,这两个参数的选取依赖于所讨论的领域知识和用户的需要,若选取不当,会对分类结果产生很大影响,从而导致不正确的分类。如何选取这两个参数的值目前尚无较好的方法,仅凭人们的经验而定,该文提出了一种应用遗传算法来优化模糊决策树中参数的方法,旨在为选取参数提供实验方法,同时也为直接选取经验参数提供了一定的实验支撑。     

构造性混合决策树

提出了一种构造性混合决策树学习方法CHDT。该方法用符号学习来进行定性分析,用神经学习进行后续的定量分析,在一定程度上模拟了人类的思维过程。CHDT采用了一种独特的构造性归纳机制,较好地解决了在缺乏领域知识指导的情况下进行构造性学习的问题,它通过采用FTART2网络和适宜于混合决策树的神经网络嵌入机制,获得了较强的泛化能力。实验结果表明,CHDT能构造出结构简洁、预测精度高的混合决策树。     

智能参数学习的模糊决策树算法

模糊决策树算法在处理数量型属性的数据时,需要进行数据模糊化预处理。但是,每个数量型属性应该模糊化为几个语言项通常要凭经验设定的,目前还没有使用标准粒子群优化算法（PSO）自动设定语言项个数的研究。提出使用PSO确定语言项个数的模糊决策树算法（FDT-K算法）,通过实验证明FDT-K算法产生的模糊决策树性能明显优于凭经验设定语言项个数所产生的模糊决策树。     

两种模糊决策树算法的对比研究

模糊决策树归纳是从具有模糊表示的示例中学习规则的一种重要方法,从符号值属性类分明的数据中提取规则可视为模糊决策树归纳的一种特殊情况。由于构建最优的模糊决策树是NP-hard,因此,针对启发式算法的研究是非常必要的。该文主要对两种启发式算法即FuzzyID3和Min-Ambiguity算法应用于符号值属性并且类分明情况所作的分析比较。通过实验与理论分析,发现FuzzyID3算法应用于符号值属性类分明的数据库时从训练准确度、测试准确度和树的规模等方面都要优于Min-Ambiguity算法。     

决策树算法的系统实现与修剪优化

决策树是对分类问题进行深入分析的一种方法，在实际问题中，按算法生成的决策树往往复杂而庞大，令用户难以理解，这就告诉我们在重分类精确性的同时，也要加强对树修剪的研究，以一个决策树算法的程序实现为例，进一步讨论了对树进行修剪优化时可能涉及的问题，目的在于给决策树研究人员提供一个深入和清晰的简化技术视图。     

动态模糊决策树学习算法研究

针对自动控制领域中普遍存在的动态模糊信息,提出了基于DFS（动态模糊集）建模的动态模糊决策树算法，并给出了对包含非动态模糊属性、缺少属性值的输入．样例的匹配算法，很好地解决了模糊控制系统所不能解决的动态性问题。     

决策树的自动生成模板

用于知识发现的大部分数据挖掘工具均采用规则发现和决策树分类技术来发现数据模式和规则。该文通过采用基于仿真属性的离散化方法,基于概率统计的未知属性与噪声数据处理方法以及基于误差的剪枝算法,实现了用于自动生成决策树的通用算法模板。利用该模板,决策树算法的设计者可以快速验证为解决特定决策问题而设计的新算法。构造决策树的基本机制是算法的设计者利用其自己定义的公式来初始化通用算法模板。然后利用该系统提供的交互式图形环境,针对不同的决策问题测试该算法,从而找出适合特定问题的算法。     

基于典型样本的Prototype决策树

文章将SVM算法和GA－NN－C4．5算法的思路结合起来，提出了用典型样本产生原型（Prototype）的方法。实验结果表明，基于典型样本的Prototype决策树搜索效果更好、判决精度更高。     

数据挖掘中决策树加权模糊熵算法

决策树算法是数据挖掘技术领域的一种重要算法 ,唐华松、姚耀文在利用熵和加权和思想的基础上提出了一种加权熵算法 ,但是此算法在解决模糊问题上有其不足之处 ,我们在加权熵算法的基础上利用模糊理论建立了一种加权模糊熵算法 ,较好的解决了这一问题。     

Advantages of decision lists and implicit negatives in Inductive Logic Programming

This paper demonstrates the capabilities offoidl, an inductive logic programming (ILP) system whose distinguishing characteristics are the ability to produce first-order decision lists, the use of an output completeness assumption as a substitute for negative examples, and the use originally motivated by the problem of learning to generate the past tense of English verbs; however, this paper demonstrates its superior performance on two different sets of benchmark ILP problems. Tests on the finite element mesh design problem show thatfoidl’s decision lists enable it to produce generally more accurate results than a range of methods previously applied to this problem. Tests with a selection of list-processing problems from Bratko’s introductory Prolog text demonstrate that the combination of implicit negatives and intensionality allowfoidl to learn correct programs from far fewer examples thanfoil. This research was supported by a fellowship from AT&T awarded to the first author and by the National Science Foundation under grant IRI-9310819. Mary Elaine Califf: She is currently pursuing her doctorate in Computer Science at the University of Texas at Austin where she is supported by a fellowship from AT&T. Her research interests include natural language understanding, particularly using machine learning methods to build practical natural language understanding systems such as information extraction systems, and inductive logic programming. Raymond Joseph Mooney: He is an Associate Professor of Computer Sciences at the University of Texas at Austin. He recerived his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 1988. His current research interests include applying machine to natural language understanding, inductive logic programming, knowledge-base and theory refinement, learning for planning, and learning for recommender systems. He serves on the editorial boards of the journalNew Generation Computing, theMachine Learning journal, theJournal of Artificial Intelligence Research, and the journalApplied Intelligence.     

An IT outsourcing cost estimation model based on Fuzzy Decision Tree

One of the most popular strategies in business today is global outsourcing. A lot of companies outsource their IT （Information Technology）. In this paper, an IT outsourcing cost estimation model based on Fuzzy Decision Tree （FDT） is presented. The model can combine inductive learning capability of FD1 with the expressive power of fuzzy sets to predict the relative error in the form of a fuzzy set and analyze the source of error using decision tree rule. Finally, the validity of the IT outsourcing cost estimation model is validated with historical project data.     

模糊划分的决策树方法

有许多优化问题中，目标值是连续的。对这类问题，首先对目标值进行离散化，再采用决策树方法提取规则。在一定程度上，相比直接对连续的目标值优化可提高正确率，并增加结果的可理解性。为了克服分段划分带来的突变性，可将目标值进行模糊划分，再采用决策树方法提取规则，这样进一步可提高正确率。     

Using Decision Trees for Agent Modeling: Improving Prediction Performance

A modeling system may be required to predict an agent's future actions under constraints of inadequate or contradictory relevant historical evidence. This can result in low prediction accuracy, or otherwise, low prediction rates, leaving a set of cases for which no predictions are made. A previous study that explored techniques for improving prediction rates in the context of modeling students' subtraction skills using Feature Based Modeling showed a tradeoff between prediction rate and predication accuracy. This paper presents research that aims to improve prediction rates without affecting prediction accuracy. The FBM-C4.5 agent modeling system was used in this research. However, the techniques explored are applicable to any Feature Based Modeling system, and the most effective technique developed is applicable to most agent modeling systems. The default FBM-C4.5 system models agents' competencies with a set of decision trees, trained on all historical data. Each tree predicts one particular aspect of the agent's action. Predictions from multiple trees are compared for consensus. FBM-C4.5 makes no prediction when predictions from different trees contradict one another. This strategy trades off reduced prediction rates for increased accuracy. To make predictions in the absence of consensus, three techniques have been evaluated. They include using voting, using a tree quality measure and using a leaf quality measure. An alternative technique that merges multiple decision trees into a single tree provides an advantage of producing models that are more comprehensible. However, all of these techniques demonstrated the previous encountered trade-off between rate of prediction and accuracy of prediction, albeit less pronounced. It was hypothesized that models built on more current observations would outperform models built on earlier observations. Experimental results support this hypothesis. A Dual-model system, which takes this temporal factor into account, has been evaluated. This fifth approach achieved a significant improvement in prediction rate without significantly affecting prediction accuracy.     

The Multi-agent Learning Model Based on Dynamic Fuzzy Logic

Machine Learning is one of the key problems of Artificial Intelligence, and the agent learning has become an important branch of machine learning. One of the main characters of intelligent agent is that it can adapt to the unknown environment. The ability to learn is the key property of agent. Because the learning act of agent is dynamic and fuzzy, this paper uses the conception of Dynamic Fuzzy Logic （DFL）tl]. Based on DFL, this paper first presents two single-agent learning algorithms, namely, single-agent leaning algorithm based on DFL with immediate reward and single-agent learning algorithm based on DFL with mediate reward. Then the paper gives a multi-agent learning model based on DFL, namely a multi-agent learning model planned on a whole. Furthermore, this paper validates that the model is useful by an example.