社会网络环境下基于公众行为大数据属性挖掘的大群体应急决策方法及应用

针对社会网络环境下复杂大群体应急决策中决策属性信息难以获得问题,提出社会网络环境下公众行为大数据驱动的大群体应急决策方法.首先,通过挖掘社交平台上的公众行为大数据,利用TF-IDF、Word2vec技术进行关键词提取、聚类及其影响力分析,从大量行为数据中挖掘大群体决策属性信息以辅助专家决策,使决策结果具有更高的科学性和有效性;其次,构建决策者间基于信任关系和观点相似度的社会网络,采用同时考虑信任和相似度的聚类方法对决策者进行聚类,并基于社会网络分析获得决策者权重;然后,提出基于决策者间信任关系的共识调整方法进行共识调整以获得最终群体决策矩阵和方案排序,通过引入决策者客观自信度避免个别决策者过分自信行为的影响;最后,通过一个新冠疫情案例分析说明方法的可行性和有效性.     

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。     

复杂大群体决策支持系统结构及实现技术研究

复杂大群体的出现给群决策支持系统的开发带来了新的问题,针对复杂大群体决策支持系统开发的复杂性,提出了一种面向复杂大群体的群决策支持系统结构,系统结构以决策问题求解为导向。首先设计了复杂决策问题求解流程和方法,然后提出了系统处理流程,在此基础上构建了系统的层次结构和功能结构,并对其中的实现技术进行了深入的研究,以期辅助面向复杂大群体的群决策支持系统的开发。     

基于云模型的不确定性大群体多属性决策方法

针对传统不确定性大群体多属性决策方法中只考虑决策信息的模糊性,没有考虑信息的随机性这一问题,提出了一种基于云模型的多属性决策方法,从而用于解决由多个小群体组成的不确定性大群体决策问题。首先将不确定语言评价值转化为一维正态云;其次采用决策者主观确定和一致性分析相结合的方法确定针对不同决策对象的小群体权重,进而生成综合云;然后提出了一种改进的云相似度算法作为云模型距离的度量,通过比较各方案综合云与最优云的相似度对方案排序。最后通过实例验证了所提方法的可行性和有效性。     

Knowledge-Based Adaptive Decision Tree State Tying for Conversational Speech Recognition

This paper presents a new method of constructing phonetic decision trees (PDTs) for acoustic model state tying based on implicitly induced prior knowledge. Our hypothesis is that knowledge on pronunciation variation in spontaneous, conversational speech contained in a relatively large corpus can be used for building domain-specific or speaker-dependent PDTs. In view of tree-structure adaptation, this method leads to transformation of tree topology in contrast to keeping fixed tree structure as in traditional methods of speaker adaptation. A Bayesian learning framework is proposed to incorporate prior knowledge on decision rules in a greedy search of new decision trees, where the prior is generated by a decision tree growing process on a large data set. Experimental results on the telemedicine automatic captioning task demonstrate that the proposed approach results in consistent improvement in model quality and recognition accuracy.     

基于累积前景理论的大群体风险型动态应急决策方法

针对复杂环境下决策者对于应急事件作出的决策往往会面对偏好转移的问题,提出一种新的大群体风险型动态应急决策方法.首先利用偏好判断矩阵对全体决策者偏好进行聚类分析和偏好集结;其次,利用累积前景理论计算决策大群体的总体前景值;再次,考虑未来状态转移链,经过多轮调整得出决策者偏好转移矩阵,结合偏好转移矩阵和大群体总体前景值可得到当前突发事件状态下的最优方案;最后,通过案例分析与对比表明所提出方法的有效性和可行性.     

一种新的基于粗糙集模型的决策树算法

在基于粗糙集模型的决策树生成算法中,由于分类的精确性,导致生成算法在对实例进行划分时往往过于细化,无法避免少数特殊实例对决策树造成的不良影响,使得生成的决策树过于庞大,不便于理解,同时也降低了其对未来数据的分类和预测能力。针对上述问题,该文给出一个新的基于粗糙集模型的决策树生成算法,引入了抑制因子。对即将扩张的结点,除了常用的终止条件外,再加入一个终止条件：若样本的抑制因子大于给定的阈值,便不再扩展该结点。有效地避免了划分过细的问题,也不会生成过于庞大的决策树,便于用户理解。     

An attitude-driven web consensus support system for heterogeneous group decision making

Consensus reaching processes are applied in group decision making problems to reach a mutual agreement among a group of decision makers before making a common decision. Different consensus models have been developed to facilitate consensus reaching processes. However, new trends bring diverse challenges in group decision making, such as the modelling of different types of information and of large groups of decision makers, together with their attitude to achieve agreements. These challenges require the capacity to deal with heterogenous frameworks, and the automation of consensus reaching processes by means of consensus support systems. In this paper, we propose a consensus model in which decision makers can express their opinions by using different types of information, capable of dealing with large groups of decision makers. The model incorporates the management of the group’s attitude towards consensus by means of an extension of OWA aggregation operators aimed to optimize the overall consensus process. Eventually, a novel Web-based consensus support system that automates the proposed consensus model is presented.     

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SPRINT方法的并行决策树分类研究

决策树技术的最大问题之一就是它的计算复杂性和训练数据的规模成正比,导致在大的数据集上构造决策树的计算时间太长。并行构造决策树是解决这个问题的一种有效方法。文中基于同步构造决策树的思想,对SPRINT方法的并行性做了详细分析和研究,并提出了进一步研究的方向。     

Portraying the practice of decision-making in requirements engineering: a case of large scale bespoke development

Complex decision-making is a prominent aspect of requirements engineering (RE) and the need for improved decision support for RE decision-makers has been identified by a number of authors in the research literature. A first step toward better decision support in requirements engineering is to understand multifaceted decision situations of decision-makers. In this paper, the focus is on RE decision-making in large scale bespoke development. The decision situation of RE decision-makers on a subsystem level has been studied at a systems engineering company and is depicted in this paper. These situations are described in terms of, e.g., RE decision matters, RE decision-making activities, and RE decision processes. Factors that affect RE decision-makers are also identified.     

Classification by clustering decision tree-like classifier based on adjusted clusters

Currently cluster analysis techniques are used mainly to aggregate objects into groups according to similarity measures. Whether the number of groups is pre-defined (supervised clustering) or not (unsupervised clustering), clustering techniques do not provide decision rules or a decision tree for the associations that are implemented. The current study proposes and evaluates a new technique to define decision tree based on cluster analysis. The proposed model was applied and tested on two large datasets of real life HR classification problems. The results of the model were compared to results obtained by conventional decision trees. It was found that the decision rules obtained by the model are at least as good as those obtained by conventional decision trees. In some cases the model yields better results than decision trees. In addition, a new measure is developed to help fine-tune the clustering model to achieve better and more accurate results.     

Decision tree induction using a fast splitting attribute selection for large datasets

Several algorithms have been proposed in the literature for building decision trees (DT) for large datasets, however almost all of them have memory restrictions because they need to keep in main memory the whole training set, or a big amount of it, and such algorithms that do not have memory restrictions, because they choose a subset of the training set, need extra time for doing this selection or have parameters that could be very difficult to determine. In this paper, we introduce a new algorithm that builds decision trees using a fast splitting attribute selection (DTFS) for large datasets. The proposed algorithm builds a DT without storing the whole training set in main memory and having only one parameter but being very stable regarding to it. Experimental results on both real and synthetic datasets show that our algorithm is faster than three of the most recent algorithms for building decision trees for large datasets, getting a competitive accuracy.     

数据挖掘技术在入侵检测中的应用与研究

决策树是数据挖掘的分类应用中采用最广泛的模型之一,但是传统的ID3、C4.5和CART等算法在应用于超大型数据库的挖掘时,有效性会降得很低,甚至出现内存溢出的现象,针对此本文提出了一种基于属性加权的随机决策树算法,并通过实验证明该算法减少了对系统资源的占用,并且对高维的大数据集具有很高的分类准确率,非常适合被用于入侵检测的分类之中。     

模糊智能决策树模型与应用研究

决策问题是计算智能最核心的问题之一.基于模糊数学理论建立了一个普适的模糊决策树模型;用节点刻画决策前提和控制信息,用树上的边形式化推理规则;并在节点和边上定义合理的模糊决策算子,进行多级综合决策.工程决策考虑不同方案的成本、可行性和收益,将这些信息进行融合作为决策方案优劣的测度;建立加权模糊智能决策模型,并给出了基于该...     

Decision forest: Twenty years of research

A decision tree is a predictive model that recursively partitions the covariate’s space into subspaces such that each subspace constitutes a basis for a different prediction function. Decision trees can be used for various learning tasks including classification, regression and survival analysis. Due to their unique benefits, decision trees have become one of the most powerful and popular approaches in data science. Decision forest aims to improve the predictive performance of a single decision tree by training multiple trees and combining their predictions. This paper provides an introduction to the subject by explaining how a decision forest can be created and when it is most valuable. In addition, we are reviewing some popular methods for generating the forest, fusion the individual trees’ outputs and thinning large decision forests.     

Enlarging the Margins in Perceptron Decision Trees

Capacity control in perceptron decision trees is typically performed by controlling their size. We prove that other quantities can be as relevant to reduce their flexibility and combat overfitting. In particular, we provide an upper bound on the generalization error which depends both on the size of the tree and on the margin of the decision nodes. So enlarging the margin in perceptron decision trees will reduce the upper bound on generalization error. Based on this analysis, we introduce three new algorithms, which can induce large margin perceptron decision trees. To assess the effect of the large margin bias, OC1 (Journal of Artificial Intelligence Research, 1994, 2, 1–32.) of Murthy, Kasif and Salzberg, a well-known system for inducing perceptron decision trees, is used as the baseline algorithm. An extensive experimental study on real world data showed that all three new algorithms perform better or at least not significantly worse than OC1 on almost every dataset with only one exception. OC1 performed worse than the best margin-based method on every dataset.     

基于属性重要度的决策树算法

传统的ID3决策树算法存在属性选择困难、分类效率不高、抗噪性能不强、难以适应大规模数据集等问题。针对该情况,提出一种基于属性重要度及变精度粗糙集的决策树算法,在去除噪声数据的同时保证了决策树的规模不会太庞大。利用多个UCI标准数据集对该算法进行了验证,实验结果表明该算法在所得决策树的规模和分类精度上均优于ID3算法。     

粗糙集理论在肝病辅助诊断中应用

粗糙集理论主要研究由论域和属性集构成的知识表达系统。医疗诊断中,大量病例、疾病症状和疾病诊断结果构成了一个医学信息决策系统。通过决策属性对条件属性依赖度和重要性分析,发现诊断结果与临床症状之间的关系,提取医学决策规则。实验表明,粗糙集用于肝病辅助诊断方法是正确可行的。     

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。