一种新的三角模糊数型多属性决策方法

针对模糊群体多属性决策问题,提出一种模糊群体多属性决策方法．首先,定义了任意专家的中心决策矩阵;其次,利用任意2个规范化决策矩阵之间的相似度获得专家的综合重要性程度;再次,通过专家的综合重要性程度得到方案的平均可信度,进而得到方案的排序结果;最后,实例分析表明了所提出方法的可行性和有效性．     

基于知识粗糙度的决策树生成方法

决策树是一种重要的数据分类方法,测试属性的选择直接影响到决策树中结点的个数和深度,本文提出了一种基于知识粗糙度的方法。通过比较我们发现：在决策树的构造上,粗集理论中知识粗糙度的方法计算量较小,构造的决策树比经典ID3算法简洁,并且具有较高的分类精度。     

基于知识粗糙度的决策树生成方法

决策树是一种重要的数据分类方法,测试属性的选择直接影响到决策树中结点的个数和深度,本文提出了一种基于知识粗糙度的方法.通过比较我们发现:在决策树的构造上,粗集理论中知识粗糙度的方法计算量较小,构造的决策树比经典ID3算法简洁,并且具有较高的分类精度.     

一种多属性决策问题的分类方法研究

针对多属性决策的分类问题,基于方案间的赋值级别高于关系,提出一种多属性决策分类法．首先描述了多属性决策的分类问题;然后通过方案间赋值的级别高于关系和线性规划模型,得到一种符合决策者偏好的多属性决策分类方法,从而对方案进行归类．该方法克服了使用传统的ELECTRE Ⅱ方法时,定义的有序方案对之间的赋值级别高于关系导出属性权重的困难,并将否决因素考虑进来．最后通过一个数值例子说明了该方法的有效性．     

On Performance Gauge of Average Multi-Cue Multi-Choice Decision Making: A Converse Lyapunov Approach

Motivated by the converse Lyapunov technique for investigating converse results of semistable switched systems in control theory,this paper utilizes a constructive induction method to identify a cost function for performance gauge of an average,multi-cue multi-choice(MCMC),cognitive decision making model over a switching time interval.It shows that such a constructive cost function can be evaluated through an abstract energy called Lyapunov function at initial conditions.Hence,the performance gauge problem for the average MCMC model becomes the issue of finding such a Lyapunov function,leading to a possible way for designing corresponding computational algorithms via iterative methods such as adaptive dynamic programming.In order to reach this goal,a series of technical results are presented for the construction of such a Lyapunov function and its mathematical properties are discussed in details.Finally,a major result of guaranteeing the existence of such a Lyapunov function is rigorously proved.     

模型决策树:一种决策树加速算法

决策树算法采用递归方法构建,训练效率较低,过度分类的决策树可能产生过拟合现象.因此,文中提出模型决策树算法.首先在训练数据集上采用基尼指数递归生成一棵不完全决策树,然后使用一个简单分类模型对其中的非纯伪叶结点(非叶结点且结点包含的样本不属于同一类)进行分类,生成最终的决策树.相比原始的决策树算法,这样产生的模型决策树能在算法精度不损失或损失很小的情况下,提高决策树的训练效率.在标准数据集上的实验表明,文中提出的模型决策树在速度上明显优于决策树算法,具备一定的抗过拟合能力.     

基于主成分分析的多变量决策树构造方法

大多数决策树构造方法在每个节点上只检验单个属性,这种单变量决策树忽视了信息系统中广泛存在的属性间的关联作用,而且修剪时往往代价很大。针对以上两点,提出了一种基于主成分分薪的多变量决策树构造方法,提取信息系统中的若干主成分来构造决策树。实验结果表明,这是一种操作简单,效率很高的决策树生成方法。     

Cervical Cancer Detection Based on Novel Decision Tree Approach

Cervical cancer is a disease that develops in the cervix’s tissue. Cervical cancer mortality is being reduced due to the growth of screening programmers. Cervical cancer screening is a big issue because the majority of cervical cancer screening treatments are invasive. Hence, there is apprehension about standard screening procedures, as well as the time it takes to learn the results. There are different methods for detecting problems in the cervix using Pap (Papanicolaou-stained) test, colposcopy, Computed Tomography (CT), Magnetic Resonance Image (MRI) and ultrasound. To obtain a clear sketch of the infected regions, using a decision tree approach, the captured image has to be segmented and analyzed. The goal of creating a decision tree is to establish prediction model that anticipate the feature vector based on the input variable. This paper deals with investigating various techniques of segmentation for detecting the cervical cancer. It proposes a novel method to develop an assistance system for the detection diagnosis of cervical cancer, based on work of Martin, Byriel and Norup. The analysis is focused on Pap smear pictures of single cells. Smear testing is a method of detecting abnormalities in the blood. Image processing is an effective method for extracting data. It is used to determine the size of cervical carcinoma and the length of the uterus. Martin’s database, which is open source and utilised for analysis and validation, is obtainable for research purposes. Cervical malignancy information utilizing three grouping strategies to anticipate the disease and afterward analyzed the outcomes showed that choice tree is the best classifier indicator with the test dataset. Further investigations ought to be led to improve execution.     

基于主成分分析的多变量决策树构造方法*

大多数决策树构造方法在每个节点上只检验单个属性,这种单变量决策树忽视了信息系统中广泛存在的属性间的关联作用,而且修剪时往往代价很大。针对以上两点,提出了一种基于主成分分析的多变量决策树构造方法,提取信息系统中的若干主成分来构造决策树。实验结果表明,这是一种操作简单,效率很高的决策树生成方法。     

Multicriteria Decision Making with Cognitive Limitations: A DS/AHP‐Based Approach

In real life, sometimes multicriteria decision making (MCDM) problems are dealt with inevitably under cognitive limitations of human's minds. However, few existing models can directly solve MCDM problems of this kind. Thus, to address the issue, this paper proposes a novel approach, which can: (i) handle the cognitive limitations in MCDM problems by distinguishing the case of complete criteria (i.e., there are no hidden cognitive factors that can deviate rational decisions) from the case of incomplete criteria (i.e., there are some hidden cognitive factors that can deviate rational decisions); (ii) differentiate incomplete and complete relative ranking of the groups of decision alternatives (DAs) over a criterion; and (iii) solve the imprecise and uncertain evaluation of criterion weight as well as the ambiguous evaluations of the groups of DAs regarding a given criterion. Hence, we give a measure to consider the influence of cognitive limitations and give two methods to reduce the influence of cognitive limitations when a decision making needs more rational. Moreover, we illustrate our approach by solving a real‐life problem of estate investment. Finally, we give some experimental results about the reduction of the required number of knowledge judgments in our method compared with the previous methods.     

一种基于决策树模型的音库构建和基元选取方法

针对嵌入式设备的存储容量小、计算能力有限的特点,设计了一种基于CART（Classification and Regression Trees）决策树模型的基元预选算法和基元选取算法,可以从原始语音语料库中挑选出最有代表性的基元样本,从而有效地降低音库规模和算法的复杂度,满足了嵌入式TFS（Text-to-Speech）系统的需要。基于以上算法,移动终端上实现了一个嵌入式中文TTS系统,实验结果表明该系统的合成语音具有较高的可懂度和自然度。     

基于决策树与相异度的离群数据挖掘方法

在数据挖掘中我们往往会忽略离群数据,可是这些数据却往往包含重要的信息.本文采用了将决策树与相异度相结合的方式进行离群数据的挖掘.通过计算决策树中各属性的信息增益,递归构造出决策树,并通过剪枝,进行初次的离群点检测,再运用相异度计算公式建立矩阵,找出最终的离群点集合.     

区间数多属性决策中一种带有可能度的排序方法

为解决具有不确定性区间数的多属性决策中的方案排序问题,在考虑落在区间内的评价值（即被认为随机变量）服从正态分布的情况下,针对方案综合评价值所在的区间存在相互交叉部分的情形,提出了关于区间数之间相互比较的可能度的概念,并结合给出了有可能度的方案排序方法。通过此方法可计算出一个方案优于另一个方案的可能度。     

基于改进决策树算法的Web数据库查询结果自动分类方法

为了解决Web数据库多查询结果问题,提出了一种基于改进决策树算法的Web数据库查询结果自动分类方法.该方法在离线阶段分析系统中所有用户的查询历史并聚合语义上相似的查询,根据聚合的查询将原始数据划分成多个元组聚类,每个元组聚类对应一种类型的用户偏好.当查询到来时,基于离线阶段划分的元组聚类,利用改进的决策树算法在查询结果集上自动构建一个带标签的分层分类树,使得用户能够通过检查标签的方式快速选择和定位其所需信息.实验结果表明,提出的分类方法具有较低的搜索代价和较好的分类效果,能够有效地满足不同类型用户的个性化查询需求.     

基于决策树ID3算法的RH-KTB系统故障诊断

介绍了RH-KTB炉外精炼真空抽气系统的工作原理和故障特点,该系统的故障诊断应满足从大量数据中快速提取征兆并及时诊断且有一定的自学习能力。提出了一种以实例为基础的归纳学习算法即ID3算法。ID3算法有一定的自学习、自组织能力,适用于复杂系统的智能诊断。以实例验证了该算法对RH-KTB真空抽气系统故障诊断的有效性。通过讨论分析了ID3算法的特点和作用。     

群决策中两类判断矩阵的一种集成方法

研究群决策中不同偏好信息形式的集成方法。根据多个决策者给出关于方案的两类偏好信息－Fuzzy判断矩阵和AHP判断矩阵,建立了能够集成这两类偏好信息的最优化模型,通过求解该模型可直接得到每个方案的参考排序列,并使方案的排序结果最大程度地反映每个决策者的偏好。     

Experience-Based Approach to Scheduling Problems With the Learning Effect

The existence of the learning effect in many manufacturing systems is undoubted; thus, it is worthwhile that it be taken into consideration during production planning to increase production efficiency. Generally, it can be done by formulating the specified problem in the scheduling context and optimizing an order of jobs to minimize the given time criteria. To carry out a reliable study of the learning effect in scheduling fields, a comprehensive survey of the related results is presented first. It reveals that most of the learning models in scheduling are based on the learning curve introduced by Wright. However, further study about learning itself pointed out that the curve may be an “S”-shaped function, which has not been considered in the scheduling domain. To fill this gap, we analyze a scheduling problem with a new experience-based learning model, where job processing times are described by “S”-shaped functions that are dependent on the experience of the processor. Moreover, problems with other experience-based learning models are also taken into consideration. We prove that the makespan minimization problem on a single processor is NP-hard or strongly NP-hard with the most of the considered learning models. A number of polynomially solvable cases are also provided.      

一种多变量决策树方法研究

单变量的决策树算法造成树的规模庞大,规则复杂,不易理解.本文结合粗糙集原理中的相对核及加权粗糙度的方法,提出了一种新的多变量决策树算法.通过实例表明,本文的多变量决策树方法产生的决策树比传统的ID3算法构造的决策树更简单,具有较好的分类效果.     

树型分类器的研究

本文从理论上给出了树分类器的数学定义,探讨了分类树的优化和性能评价函数这两个关键问题.从而提出了在卫星多光谱数据分类中切实可行的优化树分类器设计方法,包括合理的性能评价函数、新型的分类树数据结构和搜索的优化算法.最后给出了应用于森林分类的实验结果.     

A Probability-Based Approach to Comparison of Fuzzy Numbers and Applications to Target-Oriented Decision Making

In this paper, we introduce a new comparison relation on fuzzy numbers based on their alpha-cut representation and comparison probabilities of interval values. Basically, this comparison process combines a widely accepted interpretation of fuzzy sets together with the uncertain characteristics inherent in the representation of fuzzy numbers. The proposed comparison relation is then applied to the issue of ranking fuzzy numbers using fuzzy targets in terms of target-based evaluations. Some numerical examples are used to illuminate the proposed ranking technique as well as to compare with previous methods. More interestingly, according to the interpretation of the new comparison relation on fuzzy numbers, we provide a fuzzy target-based decision model as a solution to the problem of decision making under uncertainty, with which an interesting link between the decision maker's different attitudes about target and different risk attitudes in terms of utility functions can be established. Moreover, an application of the proposed comparison relation to the fuzzy target-based decision model for the problem of fuzzy decision making with uncertainty is provided. Numerical examples are also given for illustration.