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。     

Knowledge acquisition for decision support systems on an electronic assembly line

Increasing global competition has made many manufacturing companies recognize that competitive manufacturing in terms of low cost and high quality is crucial for success. Real-time process control and production optimization are, however, extremely challenging areas because manufacturing processes are getting ever more complex and involve many different parameters. This is a major problem when building decision support systems especially in electronics manufacturing. Although problem-solving is a knowledge intensive activity undertaken by people on the production floor, it is quite common to have large databases and run blindly feature extraction and data mining methods. Performance of these methods could, however, be drastically increased when combined with knowledge or expertise of the process.This paper describes how defect-related knowledge on an electronic assembly line can be integrated in the decision making process at an operational and organizational level. It focuses in particular on the efficient acquisition of shallow knowledge concerning everyday human interventions on the production lines, as well as on the factory-wide sharing of the resulting information for an improved defect management. Software with dedicated interfaces has been developed using a knowledge representation that supports portability and flexibility of the system. Semi-automatic knowledge acquisition from the production floor and generation of comprehensive reports for the quality department resulted in an improvement of the usability, usage, and usefulness of the decision support system.     

Self-instructive spreadsheets: an environment for automatic knowledge acquisition and tutor generation

Typically, spreadsheet applications are difficult to use for casual users (different from developers), mainly because of lack of support. In fact, building a tutoring facility for such applications is a time-consuming task. Our aim is the realization of a tool for the automatic generation of Intelligent Tutors for conventional spreadsheet applications. We have developed a system that works in two steps. In the first one, it extracts an explicit representation of the problem-solving pattern coded in a programmed spreadsheet. In the second step, it generates a hypertextual guide and an interactive tutor that can effectively support, in the native environment, the casual user of the spreadsheet with the specific application it is designed for. We have successfully tested our system on a class of students using an application for budget analysis.     

A decision support system for engineering design based on an enhanced fuzzy MCDM approach

Design concept is an important wealth-creating activity in companies and infrastructure. However, the process of designing is very complex. Besides, the information required during the conceptual stage is incomplete, imprecise, and fuzzy. Hence, fuzzy set theory should be used to handle linguistic problem at this stage. This paper presents a fuzzy integrated approach to assess the performance of design concepts. And those criteria rating, relative weights and performance levels are captured by fuzzy numbers, and the overall performance of each alternative is calculated through an enhanced fuzzy weighted average (FWA) approach. A practical numerical example is provided to demonstrate the usefulness of this study. In addition, this paper, in order to make computing and ranking results easier to increase the recruiting productivity, develops a computer-based decision support system to help make decisions more efficiently.     

A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets

This study investigates the effectiveness of a hybrid approach based on the artificial neural networks (ANNs) for time series properties, such as the adaptive time delay neural networks (ATNNs) and the time delay neural networks (TDNNs), with the genetic algorithms (GAs) in detecting temporal patterns for stock market prediction tasks. Since ATNN and TDNN use time-delayed links of the network into a multi-layer feed-forward network, the topology of which grows by on layer at every time step, it has one more estimate of the number of time delays in addition to several control variables of the ANN design. To estimate these many aspects of the ATNN and TDNN design, a general method based on trial and error along with various heuristics or statistical techniques is proposed. However, for the reason that determining the number of time delays or network architectural factors in a stand-alone mode does not guarantee the illuminating improvement of the performance for building the ATNN and TDNN model, we apply GAs to support optimization of the number of time delays and network architectural factors simultaneously for the ATNN and TDNN model. The results show that the accuracy of the integrated approach proposed for this study is higher than that of the standard ATNN, TDNN and the recurrent neural network (RNN).     

Applying domain knowledge and social information to product analysis and recommendations: an agent-based decision support system

Abstract: The advance of Internet and Web technologies has boosted the development of electronic commerce. More and more people have changed their traditional trading behaviors and started to conduct Internet shopping. However, the exponentially increasing product information provided by Internet enterprises causes the problem of information overload, and this inevitably reduces the customer's satisfaction and loyalty. To overcome this problem, in this paper we propose a multi‐agent system that is capable of eliciting expert knowledge and of recommending optimal products for individual consumers. The recommendations are based on both product knowledge from domain experts and the customer's preferences from system–consumer interactions. In addition, the system also uses behavior patterns collected from previous consumers to predict what the current consumer may expect. Experiments have been conducted and the results show that our system can give sensible recommendations, and it is able to adapt to the most up‐to‐date preferences for the customers.     

Modified fractal signature (MFS): a new approach to documentanalysis for automatic knowledge acquisition

One of the key technologies related to knowledge and data engineering is the acquisition of knowledge and data in the development and utilization of information system and the strategies to capture new knowledge and data. Actually, millions of documents, including technical reports, government files, newspapers, books, magazines, letters, bank checks, etc., have to be processed every day, and knowledge has to be acquired from them. This paper presents a new approach to document analysis for automatic knowledge acquisition. The traditional approaches have two major disadvantages: (1) They are not effective for processing documents with high geometrical complexity. Specially, the top-down approach can process only the simple documents which have specific format or contain some a priori information. (2) The top-down approach needs to split large components into small ones iteratively, while the bottom-up approach needs to merge small components into large ones iteratively. They are time consuming. This new approach is based on modified fractal signature. It can overcome the above weaknesses     

半指导的核聚类检测网络社团方法

近年来,复杂网络中的社团发现越来越受到研究人员的关注并且许多方法被提了出来。在这种背景下,最近李等人提出了一种用来评估社团质量的函数,称之为模块密度函数（即D值）。该函数显示了较高的D值对应于较好的社团结构,然而,优化该函数是一个NP难问题。通过模块密度函数D的半指导聚类优化,论证了模块密度函数的半指导聚类与核k方法的等价性并提出了一种新的半指导核聚类检测复杂网络社团方法。在一个经典的计算机产生的随机网络中检验了该算法,并与基于模块密度的直接核方法做了比较。特别地,当网络中社团结构变得模糊时,实验结果显示这种新的算法在发现复杂网络社团上是有效的。     

Effects of an online problem based learning course on content knowledge acquisition and critical thinking skills

This study investigated how the online problem based learning (PBL) approach employed in an online learning environment influenced undergraduate students’ critical thinking skills (CTS) and content knowledge acquisition. The pretest–posttest control group design was used in the study. The subjects included the students who were enrolled at the Department of Primary School Mathematics Teaching in Anadolu University Education Faculty. Subjects attended to Computer II course in 2008 spring. Experiment group attended the online PBL course whereas the control group attended the online instructor-led course. Each group consisted of 20 students. Data collection tools consisted of a multiple choice content knowledge acquisition scale and the Watson–Glaser critical thinking skills test. The results of two-way mixed design ANOVA indicated that learning in the online PBL group did not have a significant effect on the content knowledge acquisition scores. It was also revealed that learning in the online PBL group had a significant effect on increasing the critical thinking skills.     

A knowledge based freight management decision support system incorporating economies of scale: multimodal minimum cost flow optimization approach

This study developed a framework incorporating economies of scale into the multimodal minimum cost flow problem. To properly account for the economies of scale observed in practice, we explicitly modelled economies of scale on quantity, distance and vehicle size in a given multimodal freight network. The proposed multimodal minimum cost flow problem formulation has concave equations due to economies of scale for quantity, non-linear equations due to economies of scale for both quantity and distance, and non-continuous equations due to the economies of scale for vehicle size. A genetic algorithm was applied to find acceptable route, mode, and vehicle size choices for the multimodal minimum cost flow problem. We demonstrated how the economies of scale influenced system (mode), route choices, and total cost under various demand/service capacity scenarios. Our results will lead into more realistic assessments of intermodal system by explicitly considering the three types of economies of scale.     

A collaborative learning approach for geographic information retrieval based on social networks

Nowadays, spatial and temporal data play an important role in social networks. These data are distributed and dispersed in several heterogeneous data sources. These peculiarities make that geographic information retrieval being a non-trivial task, considering that the spatial data are often unstructured and built by different collaborative communities from social networks. The problem arises when user queries are performed with different levels of semantic granularity. This fact is very typical in social communities, where users have different levels of expertise. In this paper, a novelty approach based on three matching-query layers driven by ontologies on the heterogeneous data sources is presented. A technique of query contextualization is proposed for addressing to available heterogeneous data sources including social networks. It consists of contextualizing a query in which whether a data source does not contain a relevant result, other sources either provide an answer or in the best case, each one adds a relevant answer to the set of results. This approach is a collaborative learning system based on experience level of users in different domains. The retrieval process is achieved from three domains: temporal, geographical and social, which are involved in the user-content context. The work is oriented towards defining a GIScience collaborative learning for geographic information retrieval, using social networks, web and geodatabases.     

Building an organizational decision support system for Korea Telecom: A process redesign approach

Organizational decision support systems (ODSSs) are a new type of decision support systems (DSSs) focusing on organization-wide issues rather than individual, group, or departmental issues. Because of its organization-wide scope, a typical ODSS cuts across organizational functions or hierarchical layers. Thus, seamless integration with organization's diverse IS applications running on heterogeneous platforms becomes a critical issue for building a successful ODSS. In this paper, we analyzed the Korea Telecom's (KT) Operations > Maintenance (O&M) division focusing on its investment strategies. We developed a conceptual framework through process redesign, which links O&M investment decisions to performance of its operational branches across the nation. To support the above framework, we also developed a prototype for the KTOM-ODSS with an EIS-like user-friendly interface. When a complete ODSS is implemented on top of various KT transaction processing systems, it will become a critical component of the O > M Integrated Decision Support Environment (IDSE).     

A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach

This study presents a strategy-aligned fuzzy simple multiattribute rating technique (SMART) approach for solving the supplier/vendor selection problem from the perspective of strategic management of the supply chain (SC). The majority of supplier rating systems obtained their optimal solutions without considering firm operations management (OM)/SC strategy. The proposed system utilizes OM/SC strategy to identify supplier selection criteria. A fuzzy SMART is applied to evaluate the alternative suppliers, and deals with the ratings of both qualitative and quantitative criteria. The final decision-maker incorporates the supply risks of individual suppliers into final decision making. Finally, an empirical study is conducted to demonstrate the procedure of the proposed system and identify the suitable supplier(s).     

An approach for community detection in social networks based on cooperative games theory

Detecting communities is of great importance in social network analysis. However it is an issue that has not yet been satisfactorily solved, despite the efforts made by interdisciplinary research communities over the past few years, because of the nature of complexity in deciding how community structures should be recognized. In this paper we propose an approach based on cooperative game theory for community detection in social networks. We regard individuals as players, and regard communities as coalitions formed by players, and model community detection problem as the formation and optimization of coalitions. Furthermore, we define coalition profile for players to indicate coalitions that players joined, the order of a coalition profile is defined as the number of coalitions in a coalition profile, and we introduce a utility function to measure preference of coalition profiles. Accordingly, we propose an algorithm to detect a coalition profile with maximal utility function values. We have implemented the algorithms developed in this study and experimental results demonstrate the effectiveness of our approaches.     

Analyzing the social knowledge construction behavioral patterns of an online synchronous collaborative discussion instructional activity using an instant messaging tool: A case study

Online discussions have been widely utilized as an educational activity, and much research has been conducted on the process and behaviors involved in asynchronous discussions. However, research on behavioral patterns in learners’ synchronous discussions, including the process of social knowledge construction and project coordination is limited. Through the examination of the behavioral patterns and differences between students with high- and low-quality discussions, it may be possible to understand the limitations of knowledge construction in synchronous discussions. Furthermore, these findings may help teachers design and guide synchronous discussions activities. This study is an empirical case study in which college students conducted synchronous discussions based on topics specified by the teacher. The students used a text-based instant messaging (IM) tool in a period of 98 days.     

A decision support system for the automatic management of keep-clear signs based on support vector machines and geographic information systems

This paper presents a decision support system for automatic keep-clear signs management. The system consists of several modules. First of all, an acquisition module obtains images using a vehicle equipped with two recording cameras. A recognition module, which is based on Support Vector Machines (SVMs), analyzes each image and decides if there is a keep-clear sign in it. The images with keep-clear signs are included into a Geographical Information System (GIS) database. Finally in the management module, the data in the GIS are compared with the council database in order to decide actions such as repairing or reposition of signs, detection of possible frauds etc. We present the first tests of the system in a Spanish city (Meco, Madrid), where the systems is being tested for its application in the near future.     

An approach of support approximation to discover frequent patterns from concept-drifting data streams based on concept learning

In an online data stream, the composition and distribution of the data may change over time, which is a phenomenon known as concept drift. The occurrence of concept drift can affect considerably the performance of a data stream mining method, especially in relation to mining accuracy. In this paper, we study the problem of mining frequent patterns from transactional data streams in the presence of concept drift, considering the important issue of mining accuracy preservation. In terms of frequent-pattern mining, we give the definitions of concept and concept drift with respect to streaming data; moreover, we present a categorization for concept drift. The concept of streaming data is considered the relationships of frequency between different patterns. Accordingly, we devise approaches to describe the concept concretely and to learn the concept through frequency relationship modeling. Based on concept learning, we propose a method of support approximation for discovering data stream frequent patterns. Our analyses and experimental results have shown that in several studied cases of concept drift, the proposed method not only performs efficiently in terms of time and memory but also preserves mining accuracy well on concept-drifting data streams.     

Autonomous knowledge acquisition based on artificial curiosity: Application to mobile robots in an indoor environment

This paper describes an autonomous system for knowledge acquisition based on artificial curiosity. The proposed approach allows a humanoid robot to discover, in an indoor environment, the world in which it evolves, and to learn autonomously new knowledge about it. The learning process is accomplished by observation and by interaction with a human tutor, based on a cognitive architecture with two levels. Experimental results of deployment of this system on a humanoid robot in a real office environment are provided. We show that our cognitive system allows a humanoid robot to gain increased autonomy in matters of knowledge acquisition.     

Decision support system based on hierarchical co-evolutionary fuzzy approach: A case study in detecting gamma ray signals

Decision support systems are powerful technologies for complex decision making and problem solving. However, constructing an accurate and interpretable decision support system (DSS) for any domain is a challenge. In this paper, a novel hierarchical co-evolutionary fuzzy system called HiCEFS is presented that can autonomously derive a fuzzy rule-based DSS from exemplar data. Most of the important components in HiCEFS, including irregular shaped membership functions (ISMFs) and fuzzy rules, are generated using a hierarchical co-evolutionary genetic algorithm that simultaneously co-evolves these components in separate genetic populations. Owing to its generic learning capability, the HiCEFS approach can be easily applied to produce DSSs for classification and regression tasks in various domains. As a case study, HiCEFS is employed to construct a DSS for detecting gamma ray signals. Experimental results show that the system is able to successfully discern the gamma rays from background hadrons, and performs superior to other established techniques.