Design and evaluation of visualization support to facilitate decision trees classification

The loosely coupled relationships between visualization and analytical data mining (DM) techniques represent the majority of the current state of art in visual data mining; DM modeling is typically an automatic process with very limited forms of guidance from users. A conceptual model of the visualization support to DM modeling process and a novel interactive visual decision tree (IVDT) classification process have been proposed in this paper, with the aim of exploring humans’ pattern recognition ability and domain knowledge to facilitate the knowledge discovery process. An IVDT for categorical input attributes has been developed and experimented on 20 subjects to test three hypotheses regarding its potential advantages. The experimental results suggested that, compared to the automatic modeling process as typically applied in current decision tree modeling tools, IVDT process can improve the effectiveness of modeling in terms of producing trees with relatively high classification accuracies and small sizes, enhance users’ understanding of the algorithm, and give them greater satisfaction with the task.     

An interactive approach for multiple criteria selection problem

In this study, we develop an interactive algorithm for the multiple criteria selection problem that aims to find the most preferred alternative among a set of known alternatives evaluated on multiple criteria. We assume the decision maker (DM) has a quasi-concave value function that represents his/her preferences. The interactive algorithm selects the pairs of alternatives to be asked to the DM based on the estimated likelihood that one alternative is preferred to another. After the DM selects the preferred alternative, a convex cone is generated based on this preference information and the alternatives dominated by the cone are eliminated. Then, the algorithm updates the likelihood information for the unselected pairwise questions. The aim of the algorithm is to detect the most preferred alternative by performing as few pairwise comparisons as possible. We present the algorithm on an illustrative example problem. We also develop a mathematical model that finds the minimum number of questions that can be asked to the DM to determine the most preferred alternative under perfect information. We use the minimum number of questions to develop strategies for interactive algorithm and measure its performance.     

基于数据仓库的法院信息决策支持系统的设计

为了加快法院信息化建设,为法院决策层提供决策信息支持,提出了一种基于数据仓库的法院信息决策支持系统的总体框架。在DSS系统设计过程中,采用了OLAP+DM+Web相结合的方式,重点介绍分析了多维数据模型的设计,数据仓库的构建,以及以可视化交互的决策支持,最后通过实例展示决策支持系统的设计分析过程。     

Interactive group decision process with evolutionary database

This paper presents interactive procedures for solving a multiple criteria group decision making (MCGDM) problem with incomplete information when multiple decision makers are involved. It is difficult for group members participating in the decision making process to articulate their preferences with cardinal values. Therefore, we represent their preferences with utility ranges obtained by solving linear programming (LP) problems with incompletely specified information, find conflicting judgments, if any in their specified information, and suggest interaction processes to help the group reach a consensus. This paper will provide an algorithmic basis for a normative and interactive knowledge based group decision support system.     

A Case-Based Micro Interactive Genetic Algorithm (CBMIGA) for interactive learning and search: Methodology and application to groundwater monitoring design

Interactive optimization algorithms use real–time interaction to include decision maker preferences based on the subjective quality of evolving solutions. In water resources management problems where numerous qualitative criteria exist, use of such interactive optimization methods can facilitate in the search for comprehensive and meaningful solutions for the decision maker. The decision makers using such a system are, however, likely to go through their own learning process as they view new solutions and gain knowledge about the design space. This leads to temporal changes (nonstationarity) in their preferences that can impair the performance of interactive optimization algorithms. This paper proposes a new interactive optimization algorithm – Case-Based Micro Interactive Genetic Algorithm – that uses a case-based memory and case-based reasoning to manage the effects of nonstationarity in decision maker’s preferences within the search process without impairing the performance of the search algorithm. This paper focuses on exploring the advantages of such an approach within the domain of groundwater monitoring design, though it is applicable to many other problems. The methodology is tested under non-stationary preference conditions using simulated and real human decision makers, and it is also compared with a non-interactive genetic algorithm and a previous version of the interactive genetic algorithm.     

An interactive algorithm for portfolio bi-criteria optimization of R&D projects in public organizations

In this paper we propose an interactive algorithm for the selection of portfolios of research and development (R&D) projects in public organizations based on a bi-criteria optimization model, the need for such a model arises when the decision maker (DM) does not trust enough on the portfolio quality measure. This algorithm efficiently exploits the structure and nature of the problem to support the DM. An interesting proposal is also the representation of a portfolio as a set of “rules of support/rejection”; in this way the DM can not only valuate the portfolio by its numerical measures but also compare against his/her beliefs in a way that is more natural for him, which also allows for supporting with more arguments the solution obtained so far. Rough set methodology is employed for rule discovering. The text was submitted by autors in English.     

网上3D试衣系统技术研究

虚拟试衣系统是一种应用于服装电子商务的实时交互平台。为了满足虚拟试衣系统真实性、实时性、方便性等设计要求,该文采用服装操控技术为虚拟服装建立三维模型,并把其中的交互建模标记绑定到服装模型上,以此取代手工指定的标记,从而把原来需要人机交互的三维服装建模过程转化成一个后台计算过程。保留服装操控技术中的表面拖曳交互机制,作为用户对建模完成之后的虚拟服装的微调手段,并把原来需要人机交互指定的拖曳不动点信息固化到服装模型数据文件,以此提高虚拟试衣间实际应用中的方便性。在网上3D试衣系统的设计中,提出了布片无缝拼接算法以及二面角调整算法,并在实验中取得了较好的效果。     

Optimal consensus models for group decision making under linguistic preference relations

This paper proposes an optimal consensus model to derive weights for linguistic preference relations (LPRs). Two indexes, an individual‐to‐group consensus index (ICI) and a collective consensus index (CCI), are introduced. An iterative algorithm is presented to describe the consensus reaching process. By changing the weights and modifying a pair of individuals' comparison judgments—which have largest deviation value to the group judgments—the consensus reaching process can terminate, while both ICI and CCI are controlled with predefined thresholds. The algorithm aims to preserve the decision makers’ original information as much as possible. The model and algorithm are then extended to handle the uncertain additive LPRs. Finally, two examples are given to show the effectiveness of the proposed methods.     

数据挖掘技术在案件侦察决策中的应用

侦察决策是侦察者通过对案件进行分析后作出的侦察选择或者决定。在案件侦察过程中,采用数据挖掘技术开采海量的侦察数据,从中发现有价值的信息,是刻画案情和决策侦察行为的重要环节。因此,论文提出了一个基于数据挖掘技术的侦察决策辅助支持系统,探讨了该系统的核心功能。在数据挖掘方面,主要采用数据聚类和关联规则技术,并且通过使用案件侦破后修正规则库的手段,使该系统对规则具有一定的自我学习的智能功能。解决了海量的侦察数据与有价值的数据之间的矛盾,挖掘出可能的有价值的犯罪过程和犯罪信息,使侦察决策的形成更具有科学性和现实性。     

The methodology of multiple-criteria decision making in the optimization of an urban transportation system: case study of Poznań City in Poland

The methodology of multiple-criteria decision making applied to the optimization of an urban transportation system is presented in the paper. Three mathematical models of different complexity are constructed to optimize the allocation of vehicles to certain routes in a mass transit system. All models take into account both passengers' and operator's objectives. The optimization problems are formulated in terms of multiple-objective fuzzy linear programming and multiple-objective non-linear programming. The sensitivity and precision analysis of the models is carried out. Two interactive multi-objective mathematical programming procedures are utilized to solve the problems. They generate samples of Pareto-optimal compromise solutions and provide the decision maker (DM)with an effective tool that supports him/her in the decision making process. Finally, the DM selects the solution that best fits his or her expectations.     

多节点流水线生产订单接受与调度一体化问题

为了使企业在选择订单时获取最大利润,避免产生惩罚损失,提出了一种流水线生产企业订单接受与调度一体化的决策方法。在该方法中,将订单接受与调度同时规划,建立了以利润最大化为目的,考虑拖期惩罚的决策模型。提出一种新的基于模拟退火过程的启发式求解算法来求解该模型,实例验证了算法的有效性。     

A new approach to mining method selection based on modifying the Nicholas technique

The main purpose of this paper is to represent a solution to the problem of mining method selection (MMS) in mining projects. To this aim, the well-known MMS technique of Nicholas has been modified so that in addition to eliminate its defects, it would be possible for mining engineers to easily assign their engineering judgments to unsteady and uncertain characteristics of mineral resources. So, in order to resolve the problems of weighting of the Nicholas technique, analytic hierarchy process (AHP) as the most similar multi-criteria decision making (MCDM) tool to Nicholas technique was applied. Due to inability of crisp numbers for assigning of decision maker (DM) judgments to ambiguities of mineral resources, trapezoidal fuzzy numbers also were used for better modeling of those ambiguities. Moreover, a two-step algorithm containing hierarchical technical–operational model (HTOM) and also hierarchical economical model (HEM), inspired by Nicholas technique, was proposed. These models include some new criteria which are added to the Nicholas technique. Therefore using fuzzy AHP (FAHP), mining alternatives are firstly ranked based on HTOM and then, the most profitable of those alternatives is selected by the HEM. As a case study, the north anomaly of Choghart iron mine was used to compare the proposed approach with the Nicholas technique. The results indicated that the proposed approach eliminated the problems of Nicholas technique. Proposed approach also introduces a profitable mining alternative to start the mining operations. It should be applied to avoid further feasibility studies in mining projects.     

TOPSIS with fuzzy belief structure for group belief multiple criteria decision making

TOPSIS is one of the major techniques in dealing with multiple criteria decision making (MCDM) problems, and Belief Structure (BS) model and Fuzzy BS model have been used successfully for uncertain MCDM with incompleteness, impreciseness or ignorance. In this paper, the TOPSIS method with Fuzzy BS model is proposed to solve Group Belief MCDM problems. Firstly, the Group Belief MCDM problem is structured as a fuzzy belief decision matrix in which the judgments of each decision maker are described as Fuzzy BS models, and then the Evidential Reasoning approach is used for aggregating the multiple decision makers’ judgments. Subsequently, the positive and negative ideal belief solutions are defined with the principle of TOPSIS. In order to measure the separation from the ideal belief solutions, the concept and algorithm of Belief Distance Measure are introduced to compare the difference between Fuzzy BS models. Using the Belief Distance Measure, the relative closeness and ranking index can be calculated for ranking the alternatives. A numerical example is finally given to illustrate the proposed method.     

Interactive Particle Swarm: A Pareto-Adaptive Metaheuristic to Multiobjective Optimization

This paper proposes an interactive particle-swarm metaheuristic for multiobjective optimization (MOO) that seeks to encapsulate the positive aspects of the widely used approaches, namely, Pareto dominance and interactive decision making in its solution mechanism. Pareto dominance is adopted as the criterion to evaluate the particles found along the search process. Nondominated particles are stored in an external repository which updates continuously through the adaptive-grid mechanism proposed. The approach is further strengthened by the incorporation of a self-adaptive mutation operator. A decision maker (DM) is provided with the knowledge of an approximate Pareto optimal front, and his/her preference articulations are used to derive a utility function intended to calculate the utility of the existing and upcoming solutions. The incubation of particle-swarm mechanism for the MOO by incorporating an adaptive-grid mechanism, a self-adaptive mutation operator, and a novel decision-making strategy makes it a novel and efficient approach. Simulation results on various test functions indicate that the proposed metaheuristic identifies not only the best preferred solution with a greater accuracy but also presents a uniformly diverse high utility Pareto front without putting excessive cognitive load on the DM. The practical relevance of the proposed strategy is very high in the cases that involve the simultaneous use of decision making and availability of highly favored alternatives.     

INTERACTIVE DECISION MAKING FOR MULTI-OBJECTIVE LINEAR FRACTIONAL PROGRAMMINGPROBLEMS WITH FUZZY PARAMETERS

Abstract

In this paper, we focus on multiobjective linear fractional programming problems with fuzzy parameters and present a new interactive decision making method for obtaining the satisficing solution of the decision maker (DM) on the basis of the linear programming method. The fuzzy parameters in the objective functions and the constraints are characterized by fuzzy numbers. The concept of a-Pareto optimality is introduced in which the ordinary Pareto optimality is extended based on the α-level sets of the fuzzy numbers. In our interactive decision making method, in order to generate a candidate for the satisficing solution which is also a-Pareto optimal, if the DM specifies the degree α of the a-level sets and the reference objective values, the minimax problem is solved by combined use of the bisection method and the linear programming method and the DM is supplied with the corresponding α-Pareto optimal solution together with the trade-off rates among the values of the objective functions and the degree a. Then by considering the current values of the objective functions and a as well as the trade-off rates, the DM acts on this solution by updating his/her reference objective values and/or degree a. In this way the satisficing solution for the DM can be derived efficiently from among an a-Pareto optimal solution set. A numerical example illustrates various aspects of the results developed in this paper.     

Enhancing the Effectiveness of Interactive Case-Based Reasoning with Clustering and Decision Forests

In interactive case-based reasoning, it is important to present a small number of important cases and problem features to the user at one time. This goal is difficult to achieve when large case bases are commonplace in industrial practice. In this paper we present our solution to the problem by highlighting the interactive user- interface component of the CaseAdvisor system. In CaseAdvisor, decision forests are created in real time to help compress a large case base into several small ones. This is done by merging similar cases together through a clustering algorithm. An important side effect of this operation is that it allows up-to-date maintenance operations to be performed for case base management. During the retrieval process, an information-guided subsystem can then generate decision forests based on users' current answers obtained through an interactive process. Possible questions to the user are carefully analyzed through information theory. An important feature of the system is that case-base maintenance and reasoning are integrated in a seamless whole. In this article we present the system architecture, algorithms as well as empirical evaluations.     

PROJECT Method for Multiobjective Optimization Based on Gradient Projection and Reference Points

In this paper, we propose a new interactive method for multiobjective programming (MOP) called the PROJECT method. Interactive methods in MOP are techniques that can help the decision maker (DM) to generate the most preferred solution from a set of efficient solutions. An interactive method should be capable of capturing the preferences of the DM in a pragmatic and comprehensive way. In certain decision situations, it may be easier and more reliable for DMs to follow an interactive process for providing local tradeoffs than other kinds of preferential information like aspiration levels, objective function classification, etc. The proposed PROJECT method belongs to the class of interactive local tradeoff methods. It is based on the projection of utility function gradients onto the tangent hyperplane of an efficient set and on a new local search procedure that inherits the advantages of the reference-point method to search for the best compromise solution within a local region. Most of the interactive methods based on local tradeoffs assume convexity conditions in a MOP problem, which is too restrictive in many real-life applications. The use of a reference-point procedure makes it possible to generate any efficient solutions, even the nonsupported solutions or efficient solutions located in the nonconvex part of the efficient frontier of a nonconvex MOP problem. The convergence of the proposed method is investigated. A nonlinear example is examined using the new method, as well as a case study on efficiency analysis with value judgements. The proposed PROJECT method is coded in Microsoft Visual C++ and incorporated into the software PROMOIN (Interactive MOP).     

Sequential revision of belief: an application to complex decisionmaking situations

This research addresses the process of sequential revision of beliefs or judgments in complex situations. The task domain, military command and control, provides decision makers with opportunities to revise their tactical judgments as streams of information flow in for their consideration. A contrast-inertia model is proposed that describes subject's sequential revision of beliefs exhibited by subjects and a resulting order-effect that is observed when subjects attempt to integrate pieces of confirming and disconfirming evidence. Two experiments were conducted to test the predictions of the contrast-inertia model and to investigate various aspects of the order effect. The experiments manipulated the initial starting position or anchor against which subjects contrast new evidence to revise their beliefs. Results form both experiments showed strong recency and order effects when subjects integrated inconsistent pieces of evidence sequentially, regardless of the initial anchor. Moreover, the contrast-inertia model fit the experimental data very well and confirmed the basic assumptions predicting an order effect     

Interactive fuzzy stochastic multi-level 0–1 programming using tabu search and probability maximization

In this paper, we consider interactive fuzzy programming for multi-level 0–1 programming problems involving random variable coefficients both in objective functions and constraints. Following the probability maximization model together with the concept of chance constraints, the formulated stochastic multi-level 0–1 programming problems are transformed into deterministic ones. Taking into account vagueness of judgments of the decision makers, we present interactive fuzzy programming. In the proposed interactive method, after determining the fuzzy goals of the decision makers at all levels, a satisfactory solution is derived efficiently by updating satisfactory levels of the decision makers with considerations of overall satisfactory balance among all levels. For solving the transformed deterministic problems efficiently, we also introduce novel tabu search for general 0–1 programming problems. A numerical example for a three-level 0–1 programming problem is provided to illustrate the proposed method.     

Random fuzzy multi-objective linear programming: Optimization of possibilistic value at risk (pVaR)

This paper considers multiobjective linear programming problems (MOLPP) where random fuzzy variables are contained in objective functions and constraints. A new decision making model optimizing possibilistic value at risk (pVaR) is proposed by incorporating the concept of value at risk (VaR) into possibility theory. It is shown that the original MOLPPs involving random fuzzy variables are transformed into deterministic problems. An interactive algorithm is presented to derive a satisficing solution for a decision maker (DM) from among a set of Pareto optimal solutions. Each Pareto optimal solution that is a candidate of the satisficing solution is exactly obtained by using convex programming techniques. A simple numerical example is provided to show the applicability of the proposed methodology to real-world problems with multiple objectives in uncertain environments.