粒度决策演化模型的博弈选择研究

粒度决策演化模型是基于时间序列对粗糙集在动态数据预测方面的一种方法,对于在处理动态数据方面该模型有着较好的效果。但是在预测过程中出现属性支持度相同的属性时如何解决冲突模型并未说明,在粒度决策演化模型的基础上利用博弈论方法对解决这种冲突进行研究。     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

A hybrid decision making approach to prevent chatter vibrations

In this paper, the optimum cutting conditions without chatter vibrations have been determined during turning operations. Chatter vibrations are detrimental and cause poor surface properties. In this study, chatter vibration prevention has been discussed in a different way using a multi-criteria decision making approach. Regression-multi-criteria decision making hybrid models have been developed and applied to the problem of chatter vibrations. First, regression models have been used to determine the criteria weights for TOPSIS (technique for order preference by similarity to ideal solution) model. Then, TOPSIS models have been developed. Three different hybrid models have been studied. The results of these three models are the same. It has been seen from the results that the number of revolutions and the workpiece hardness are the most effective parameters. The models are developed to help operators in different manufacturing environments.     

决策演化集的三支博弈

决策演化集是处理决策规则在时间序列上的演化问题的理论。决策演化集将着眼点从静态的决策信息系统转移到动态的时间序列上,研究决策信息系统在随着时间变化时的演化规律,是一种新的决策研究方法。目前,在决策演化集的标准结构下存在着一些问题,例如预测得到的属性较少,预测夹角偏大等问题。决策演化集的三支结构在提高预测准确度方面是一个有效的方法,但其阈值α和β是固定的。然而,在时间序列下数据是不停变化的,固定的α和β并不能很好地适应这种变化。利用博弈论的方法来调整修改α和β使其适应决策信息系统在时间序列下的变化,并通过实例来演示这种调整。     

The impact of problem size on decision processes: an experimental investigation on very large choice problems with support of decision support systems

Abstract: Choice problems as a class of decision problems have attracted great attention for the last couple of decades. Among the frameworks and supporting theories used in their study, two have had the greatest impact: bounded rationality and cost–benefit. Both theories could find support from past empirical studies under different conditions or problem environments. In the past studies, problem size has been shown to play an important role in decision‐making. As problem size increases, a decision process may be detoured and the decision outcome may be different. In this paper we investigate the impact of problem size on three important aspects of the computer‐aided decision process – strategy selection, decision time/effort, and decision quality – through very large choice problems.     

高效帧内预测模式选择算法

帧内预测技术是H.264不同于以往标准的特点之一.在深入分析帧内预测原理以及预测模式选择过程的基础上,提出了一种新的帧内预测模式选择算法.算法通过相邻宏块预测模式相关性原则、预测模式尺寸预先选择原则、提前中止原则来加快编码速度.实验结果表明,与全搜索算法相比,在编码质量相近的情况下,能够节省51.01%～72.91%的编码时间,与FIMDA算法相比,能够节省15.63%～27.31%的编码时间.     

A knowledge-based decision support system to analyze the debris-flow problems at Chen-Yu-Lan River, Taiwan

Decision-making for the debris-flow management involves multiple decision-makers often with concerning geomorphological and hydraulic conditions. Spatial decision support systems (SDSS) can be developed to improve our understanding of the relations among the natural and socio-economic variables to the occurrence/non-occurrence samples of debris-flow. Accordingly, the goal of this study is to development a debris-flow decision support system to manage and monitor the debris-flows in Nan-Tou County, Taiwan. The present study, more specifically, combines a spatial information system with an advanced Data Mining technique to investigate the debris-flow problem. In the first stage, our spatial information system integrates remote sensing, DEM, and aerial photos as three different resources to generate our spatial database. Each of the geomorphological and hydraulic attributes are obtained automatically through our spatial database. Then, a Data Mining classifier (hybrid model of decision tree (D.T.) + support vector machine (S.V.M.)) will be used to analyze and resolve the classification of occurrence of debris-flow. The contribution of this study has found that watershed area and NDVI (Normalized Difference Vegetation Index) are the crucial factors governing debris-flow by means of decision tree analysis. Further, the performance of prediction accuracy on testing samples through support vector machine is 73% which could be helpful for us to have better understanding of debris-flow problem.     

基于本体和CBR的钻井工程风险决策模型研究

为了实现钻井工程风险智能决策,从钻井专家决策过程的特征出发,针对传统的CBR技术的不足,提出并构建了一种基于本体和CBR的钻井工程风险决策模型。通过对风险决策案例的分析,设计了钻井风险决策案例的结构,并结合本体技术构建了风险决策案例本体,使案例表示更规范化、语义化,同时为案例知识提供了良好的可扩展性与共享性;为了提高案例检索效率,按照本体中风险的类别对案例进行分层组织,建立了风险案例库;针对现有的案例相似度计算模型的缺陷,提出了一种改进的基于本体的语义相似度计算模型,并由此构建了风险案例检索模型,经现场实例测试,结果表明该模型有效地提高了案例检索的查准率和查全率。在以上基础上,开发了钻井工程风险决策原型系统,为钻井专家、技术人员提供了高效的决策支持。     

一种粗糙集-决策树结合的入侵检测方法

针对入侵检测系统收集数据海量、高维、检测模型复杂和检测准确率低等问题,采用粗糙集属性约简的优势寻找与判断入侵与否相关的属性,利用决策树分类算法生成模型并对网络连接进行入侵预测分类检测,从而提出了一种粗糙集属性约简和决策树预测分类相结合的网络入侵检测方法.实验结果表明,该方法在入侵检测准确率上有很大的提高,对DoS攻击、Probe攻击和R2L攻击的检测效果均有所提高,同时大大降低了检测的误报率.     

A general model for fuzzy decision tree and fuzzy random forest

The problem of risk classification and prediction, an essential research direction, aiming to identify and predict risks for various applications, has been researched in this paper. To identify and predict risks, numerous researchers build models on discovering hidden information of a label (positive credit or negative credit). Fuzzy logic is robust in dealing with ambiguous data and, thus, benefits the problem of classification and prediction. However, the way to apply fuzzy logic optimally depends on the characteristics of the data and the objectives, and it is extraordinarily tricky to find such a way. This paper, therefore, proposes a general membership function model for fuzzy sets (GMFMFS) in the fuzzy decision tree and extend it to the fuzzy random forest method. The proposed methods can be applied to identify and predict the credit risks with almost optimal fuzzy sets. In addition, we analyze the feasibility of our GMFMFS and prove our GMFMFS‐based linear membership function can be extended to a nonlinear membership function without a significant increase in computing complex. Our GMFMFS‐based fuzzy decision tree is tested with a real dataset of US credit, Susy dataset of UCI, and synthetic datasets of big data. The results of experiments further demonstrate the effectiveness and potential of our GMFMFS‐based fuzzy decision tree with linear membership function and nonlinear membership function.     

Cloud-based decision support systems and availability context: the probability of successful decision outcomes

In an age of cloud computing, mobile users, and wireless networks, the availability of decision support related computing resources can no longer guarantee five-nines (99.999%) availability. Since the dependence on decision support systems is ever increasing, obtaining accurate deterministic advice from these systems will become critical. This study proposes a probabilistic model that maps decision resource availability to correct decision outcomes. Grounded in system reliability theory, the probability functions are given and developed. The model is evaluated with a simulated decision opportunity and the outcome of the experimentation is quantified using a goodness of fit measure and ANOVA testing.     

作战指挥的可拓决策方法

作战决策是作战指挥的核心。论述了作战指挥中的决策问题,分析了决策思维的逻辑结构和作战指挥决策的内涵及特点。引入可拓学理论与方法对作战决策问题进行形式化描述,建立了基于可拓决策方法的作战决策方案生成与评价模型,并结合案例分析,说明了该方法的可行性和有效性。可应用于作战模拟与决策支持系统。     

融合三支决策与GRA-Orthopair模糊集的知识匹配方法研究

为缓解用户"知识迷向"问题,提升知识应用及创新效益,提出了融合三支决策与GRA-Orthopair模糊集的知识匹配方法.首先基于CRITIC-熵权法进行权重配置,并融入博弈论方法增强客观赋权实效;而后,采用FCM聚类算法横向压缩匹配空间;进而,借鉴三支决策思想实施预分类,同时考虑知识间的关联度,构建基于灰色关联度的Orthopair模糊相似度模型,并据此计算用户需求与案例知识间的视图相似度,从而确定匹配结果.实验结果表明,该算法的知识匹配精度及效率优于传统匹配算法,提升了匹配效果.     

基于机器学习的医疗决策支持系统综述

利用健康医疗领域的海量临床数据进行辅助医疗决策支持是智慧医疗的核心技术和必然的发展趋势。医疗决策支持主要包括疾病风险预测与疾病智能诊断两方面，以临床积累和实时获取的多种数据来源为基础，通过多种机器学习算法实现对患者疾病类型的分类或者对患病风险的预测。从医疗决策支持的概念和方法框架出发，按照不同疾病种类，总结了当前采用的机器学习诊断和预测方法，着重介绍这些方法的特点和区别，并对存在的挑战和未来发展进行分析。     

自动驾驶中量子认知与决策问题研究

自动驾驶车辆对人类驾驶车辆和行人的意图估计及其相互作用研究是极其重要的,现有的研究不能很好的解释人类交通参与者的不确定因素和非理性行为,这对研究自动驾驶车辆在真实道路交通场景中运行形成了阻碍,本文基于量子理论和锚定效应,针对自动驾驶车辆右转时与非机动车和行人交互场景,构建量子决策模型.仿真分析和数据集实验证明了在与人类交通参与者进行交互时,锚定效应下的量子决策模型可以考虑存在不确定性因素和非理性行为时进行加速或减速的决策,且相比于累积前景理论模型(CPT)更加贴合实际情况.     

A comparative study on consensus measures in group decision making

Decision situations in which several individual are involved are known as group decision‐making (GDM) problems. In such problems, each member of the group, recognizing the existence of a common problem, tries to come to a collective decision. A high level of consensus among experts is needed before reaching a solution. It is customary to construct consensus measures by using similarity functions to quantify the closeness of experts preferences. The use of a metric that describes the distance between experts preferences allows the definition of similarity functions. Different distance functions have been proposed in order to implement consensus measures. This paper examines how the use of different aggregation operators affects the level of consensus achieved by experts through different distance functions, once the number of experts has been established in the GDM problem. In this situation, the experimental study performed establishes that the speed of the consensus process is significantly affected by the use of diverse aggregation operators and distance functions. Several decision support rules that can be useful in controlling the convergence speed of the consensus process are also derived.     

Norms in artificial decision making

A method for forcing norms onto individual agents in a multi-agent system is presented. The agents under study are supersoft agents: autonomous artificial agents programmed to represent and evaluate vague and imprecise information. Agents are further assumed to act in accordance with advice obtained from a normative decision module, with which they can communicate. Norms act as global constraints on the evaluations performed in the decision module and hence no action that violates a norm will be suggested to any agent. Further constraints on action may then be added locally. The method strives to characterise real-time decision making in agents, in the presence of risk and uncertainty.     

What is on your mind? Using the perceptual cycle model and critical decision method to understand the decision-making process in the cockpit

Aeronautical decision-making is complex as there is not always a clear coupling between the decision made and decision outcome. As such, there is a call for process-orientated decision research in order to understand why a decision made sense at the time it was made. Schema theory explains how we interact with the world using stored mental representations and forms an integral part of the perceptual cycle model (PCM); proposed here as a way to understand the decision-making process. This paper qualitatively analyses data from the critical decision method (CDM) based on the principles of the PCM. It is demonstrated that the approach can be used to understand a decision-making process and highlights how influential schemata can be at informing decision-making. The reliability of this approach is established, the general applicability is discussed and directions for future work are considered.

Practitioner Summary: This paper introduces the PCM, and the associated schema theory, as a framework to structure and explain data collected from the CDM. The reliability of both the method and coding scheme is addressed.     

基于决策理论的指令集识别技术研究

针对所属处理器指令集未知的二进制目标代码无法直接进行处理和分析的问题,研究了一种基于决策理论的、在反汇编器的支持下的,通过对程序流的分析及信息获取进行处理器指令集识别的方法,提出了相似度的概念,阐述了依据相似度评估判定未知二进制目标代码所属指令集的思想,并描述了一种可实现的算法。实验结果表明,该方法具有较高的识别准确率和良好的应用价值。     

Artificial intelligence and Internet of Things-enabled decision support system for the prediction of bacterial stalk root disease in maize crop

Although the Internet of Things (IoT) has been considered one of the most promising technologies to automate various daily life activities, that is, monitoring and prediction, it has become extremely useful for problem solving with the introduction and integration of artificial intelligence (AI)-enabled smart learning methodologies. Therefore, due to their overwhelming characteristics, AI-enabled IoTs have been used in different application environments, such as agriculture, where detection, prevention (if possible), and prediction of crop diseases, especially at the earliest possible stage, are desperately required. Bacterial stalk root is a common disease of tomatoes that severely affects its production and yield if necessary measures are not taken. In this article, AI and an IoT-enabled decision support system (DSS) have been developed to predict the possible occurrence of bacterial stalk root diseases through a sophisticated technological infrastructure. For this purpose, Arduino agricultural boards, preferably with necessary embedded sensors, are deployed in the agricultural field of maize crops to capture valuable data at a certain time interval and send it to a centralized module where AI-based DSS, which is trained on an equally similar data set, is implemented to thoroughly examine captured data values for the possible occurrence of the disease. Additionally, the proposed AI- and IoT-enabled DSS has been tested on benchmark data sets, that is, freely available online, along with real-time captured data sets. Both experimental and simulation results show that the proposed scheme has achieved the highest accuracy level in timely prediction of the underlined disease. Finally, maize crop plots with the proposed system have significantly increased the yield (production) ratio of crops.