不协调广义多尺度决策系统的最优尺度组合的层次关系

多尺度信息系统是一类特殊的对象-属性值系统,数据集中的每个对象在每个属性下根据不同的尺度或粒度可呈现出不同的值,并从细粒度属性值域到粗粒度属性值域间存在粒信息变换函数.从多尺度数据集中的每个属性中选择一个满足预设条件的尺度用于最终的决策分析(这个过程称为最优尺度组合选择)是多尺度决策系统知识获取的关键问题.针对不协调广义多尺度决策系统的最优尺度组合选择问题,首先,通过引入三层思维提出广义决策类最优尺度组合和对象最优尺度组合的概念,讨论两者间的层次关系;然后,提出属性约简诱导的最优尺度组合和关键尺度组合的概念,讨论对象关键尺度组合与广义决策类关键尺度组合间的层次关系;最后,依据对象关键尺度组合与广义决策类关键尺度组合间的层次关系给出两者间互相计算的方法.     

决策表属性约简的相对信息量表示

知识约简是粗糙集理论研究的主要内容之一,该文在信息系统中引入了知识的相对信息量的概念。对一致决策表,证明了其属性约简在代数表示下和相对信息量表示下是等价的,但对不一致决策表,举例说明其属性约简的代数表示不能用相对信息量来等价表示。由此可见,相对信息量表示比代数表示直观,但不能完全代替代数表示方法。     

基础信息层经验规则的一种表达方法

该文讨论在复杂的大型辅助决策系统中,构造智能决策规则模型的一种方法。这是一种基于决策表的知识表示方法。它在传统决策表的基础上,吸收了产生式规则、框架表示法、模糊理论、关系模型等多种方法的思想和技术,把传统决策表加以扩展,得到了一种结构性好、表达能力强、可操作性较好的智能决策表达工具,用来表示大型辅助决策系统中的复杂领域知识,将其中松散的经验规则形式化成智能决策规则模型,从而增强其结构性和可操作性,有效支持对其它信息的操作。     

一种分布式的决策表核属性计算方法

针对决策表核属性的计算问题,首先对前人的一些结论观点进行了讨论,在认识这些理论观点缺陷的基础上,给出了分明矩阵的一个新的表示定义,并由此提出了一种新的核属性计算方法。然后依据论域中各对象在某个条件属性上的不同取值把决策表信息系统划分为多个子决策表信息系统,给出了几条相关性质以及子决策表与原决策表核属性的关系定理。最后提出了一种决策表核属性的分布式计算方法,实例分析表明该算法是有效的。     

基于粗集理论的决策表知识获取方法研究

对决策表各条件分类和决策分类集合之间的关系进行了研究,提出了直接从各分类中计算决策表核及属性约简方法：依据属性约简,创建了一个多变量决策树;在此基础上,阐述了提取决策表中蕴含规则的方法,从而省去了在约简后的决策表中计算值约简步骤;通过实例,验证了这些方法的有效性。     

决策表属性约简及其条件信息量表示

讨论了决策表属性约简和条件信息量之间的关系,证明了对于一致决策表,其属性约简在代数表示下和信息论中条件信息量表示下是等价的。并举例说明,对于不一致决策表,其属性约简的代数表示不能用条件信息量来等价表示。论文还给出了一种基于条件信息量的属性约简启发式算法。     

决策表中粗糙集的布尔矩阵表示

建立了布尔矩阵与逻辑方程组的解和决策表中的属性集之间的关系;然后在此基础上给出了决策表中的粗糙集理论的布尔矩阵表示;最后证明了属性约简在布尔矩阵和代数两种不同表示下是等价的。这些结论有助于人们深刻理解粗糙集理论的本质,同时为寻找高效的属性约简算法奠定了基础。     

Table-based representations can be used to offer easy-to-use,flexible, and adaptable learning scenario editors

This article presents an innovative approach to the design of learning scenario editors for teachers as not-specifically-trained users. The approach features simplicity, flexibility, and easy adaptation to local contexts and needs. It is based on the governing design decision to provide a basic representation device known to be easy to use and adopted by teachers in standard practice: a table, similar to that found in office suites. The table is put in structural correspondence with a pivotal model (a tree), which is used to implement services requiring complex mechanisms that can be found in state-of-the-art systems, for example instantiation support or operationalization. We show how this approach makes it possible to design simple, flexible editors that offer complex services as add-ons and are easy to adapt to users' local needs, practices, and/or technical ecosystems.     

大型决策表分解方法研究

数据的海量性和复杂性是当前决策表数据分析中面临的难题,分解是处理大型决策表复杂特性、提高分析效率和质量的有效手段.讨论了大型决策表分析存在的问题和决策表分解的必要性,提出了评价分解方法的三条标准,重点对几种决策表分解方法进行了分析和比较,指出了其特点与不足,提出了进一步研究的方向.     

基于水平划分决策表的核属性求解算法

核属性求解是粗糙集理论的主要研究内容之一。针对现有差别矩阵求核算法的不足,给出决策差别矩阵定义和水平划分决策表方法。提出在子决策表上创建子决策差别矩阵,进行核属性求解的方法;并证明了由该方法获得核与正区域核是等价的,同时设计相应的串行和并行求核算法。实例分析和实验比较表明所提出的求核算法是正确的、高效的。     

Functional Trees

In the context of classification problems, algorithms that generate multivariate trees are able to explore multiple representation languages by using decision tests based on a combination of attributes. In the regression setting, model trees algorithms explore multiple representation languages but using linear models at leaf nodes. In this work we study the effects of using combinations of attributes at decision nodes, leaf nodes, or both nodes and leaves in regression and classification tree learning. In order to study the use of functional nodes at different places and for different types of modeling, we introduce a simple unifying framework for multivariate tree learning. This framework combines a univariate decision tree with a linear function by means of constructive induction. Decision trees derived from the framework are able to use decision nodes with multivariate tests, and leaf nodes that make predictions using linear functions. Multivariate decision nodes are built when growing the tree, while functional leaves are built when pruning the tree. We experimentally evaluate a univariate tree, a multivariate tree using linear combinations at inner and leaf nodes, and two simplified versions restricting linear combinations to inner nodes and leaves. The experimental evaluation shows that all functional trees variants exhibit similar performance, with advantages in different datasets. In this study there is a marginal advantage of the full model. These results lead us to study the role of functional leaves and nodes. We use the bias-variance decomposition of the error, cluster analysis, and learning curves as tools for analysis. We observe that in the datasets under study and for classification and regression, the use of multivariate decision nodes has more impact in the bias component of the error, while the use of multivariate decision leaves has more impact in the variance component.     

A new data structure for cumulative frequency tables

A new method (the ‘binary indexed tree’) is presented for maintaining the cumulative frequencies which are needed to support dynamic arithmetic data compression. It is based on a decomposition of the cumulative frequencies into portions which parallel the binary representation of the index of the table element (or symbol). The operations to traverse the data structure are based on the binary coding of the index. In comparison with previous methods, the binary indexed tree is faster, using more compact data and simpler code. The access time for all operations is either constant or proportional to the logarithm of the table size. In conjunction with the compact data structure, this makes the new method particularly suitable for large symbol alphabets.     

CAD系统中公差信息建模技术综述

公差信息建模是指对某一些造型系统进行准确无误的公差表述,并对其语义作出正确合理的解释。由于某对CAD／CAPP／CAM的集成有着十分重要的作用。因此一直是CAD／CAM研究领域的热点,研究成果十分丰硕。另一方面,由于公差信息的建模有着相当的难度,该领域至今仍然有着许多问题有待解决。文中对当前CAD系统中公差信息建模技术的研究历史和现状进行了较为全面的综述,分析比较了目前各种模型的优点与不足,最后对公差信息建模中亟待解决的问题及可能对策进行了讨论。     

The impact of color combinations on the legibility of text presented on LCDs

The paper investigates the impact of text and background color combinations on the legibility of text presented on LCDs. The legibility of 56 elementary color combinations was tested by 308 participants. The first task required the participants to identify 21 uppercase alphabetic characters selected and presented in conformance with a Snellen chart in various color combinations. For each color combination, the number of correctly identified characters was recorded as a visual performance measure. In the second task, participants subjectively rated the legibility of color combinations on a ten-point Likert scale. The results show that neither the Le Courier legibility table nor the CRT legibility table is appropriate for LCDs. A new legibility table is proposed for LCDs where the highest ranked are contrastive color combinations with positive polarity whereas for CRT displays, the highest ranked are contrastive color combinations with negative polarity. The findings of this study can be used to determine the best possible color combinations when developing content displayed on LCDs.     

决策表化简及其属性约简方法

给出一种求不一致决策表的核和所有属性约简的新方法,首先利用U/C对决策表进行化简,然后在简化的决策表上定义新的差别矩阵,最后利用差别函数求出所有属性约简.该方法可有效降低生成差别矩阵时所需的存储空间和时间,理论分析和例子表明,该属性约简方法在效率上比现有的基于差别矩阵和差别函数的方法有显著提高.     

The hybrid representation model for web document classification

Most Web content categorization methods are based on the vector space model of information retrieval. One of the most important advantages of this representation model is that it can be used by both instance‐based and model‐based classifiers. However, this popular method of document representation does not capture important structural information, such as the order and proximity of word occurrence or the location of a word within the document. It also makes no use of the markup information that can easily be extracted from the Web document HTML tags. A recently developed graph‐based Web document representation model can preserve Web document structural information. It was shown to outperform the traditional vector representation using the k‐Nearest Neighbor (k‐NN) classification algorithm. The problem, however, is that the eager (model‐based) classifiers cannot work with this representation directly. In this article, three new hybrid approaches to Web document classification are presented, built upon both graph and vector space representations, thus preserving the benefits and overcoming the limitations of each. The hybrid methods presented here are compared to vector‐based models using the C4.5 decision tree and the probabilistic Naïve Bayes classifiers on several benchmark Web document collections. The results demonstrate that the hybrid methods presented in this article outperform, in most cases, existing approaches in terms of classification accuracy, and in addition, achieve a significant reduction in the classification time. © 2008 Wiley Periodicals, Inc.     

基于数据的旅游管理决策支持系统研究

针对复杂环境下的旅游管理决策问题, 对基于数据的智能决策支持系统进行研究. 首先提出了综合多种智能技术的决策支持系统体系结构, 对系统涉及到的3S、大型决策表分解等关键技术进行了分析; 然后从旅游信息类别、时空模式、旅游规划与导航, 以及旅游状态和安全事故预测预警等方面给出了系统采用的数据分析方法, 并给出了系统的多个应用实例.     

Constructing X-of-N Attributes for Decision Tree Learning

While many constructive induction algorithms focus on generating new binary attributes, this paper explores novel methods of constructing nominal and numeric attributes. We propose a new constructive operator, X-of-N. An X-of-N representation is a set containing one or more attribute-value pairs. For a given instance, the value of an X-of-N representation corresponds to the number of its attribute-value pairs that are true of the instance. A single X-of-N representation can directly and simply represent any concept that can be represented by a single conjunctive, a single disjunctive, or a single M-of-N representation commonly used for constructive induction, and the reverse is not true. In this paper, we describe a constructive decision tree learning algorithm, called XofN. When building decision trees, this algorithm creates one X-of-N representation, either as a nominal attribute or as a numeric attribute, at each decision node. The construction of X-of-N representations is carried out by greedily searching the space defined by all the attribute-value pairs of a domain. Experimental results reveal that constructing X-of-N attributes can significantly improve the performance of decision tree learning in both artificial and natural domains in terms of higher prediction accuracy and lower theory complexity. The results also show the performance advantages of constructing X-of-N attributes over constructing conjunctive, disjunctive, or M-of-N representations for decision tree learning.     

基于粗糙集和多通道词向量的中文文本情感特征分析

粗糙集是一种能够有效处理不精确、不完备和不确定信息的数学工具,粗糙集的属性约简可以在保持文本情感分类能力不变的情况下对文本情感词特征进行约简。针对情感词特征空间维数过高、情感词特征表示缺少语义信息的问题,该文提出了RS-WvGv中文文本情感词特征表示方法。利用粗糙集决策表对整个语料库进行情感词特征建模,采用Johnson粗糙集属性约简算法对决策表进行化简,保留最小的文本情感词特征属性集,之后再对该集合中的所有情感特征词进行词嵌入表示,最后用逻辑回归分类器验证RS-WvGv方法的有效性。另外,该文还定义了情感词特征属性集覆盖力,用于表示文本情感词特征属性集合对语料库的覆盖能力。最后,在实验对比的过程中,用统计检验进一步验证了该方法的有效性。     

基于分解合并策略的属性约简算法

在基于粗集理论的知识获取研究中,属性约简是最核心的工作之一。结合分治法的思想,从论域划分的角度将一个大的决策表分解成两个子决策表,并利用经典的属性约简算法计算两个子决策表的约简,在此基础上利用合并约简算法将这两个子决策表合并,并求出原问题的解。该方法为解决大数据集的属性约简提供了一个新的途径。实验说明了算法的有效性。