Jae Kyeong Kim

An interactive procedure for multiple attribute group decision making with incomplete information: Range-based approach

This paper presents an interactive procedure for solving a multiple attribute group decision making (MAGDM) problem with incomplete information. The main properties of the procedure are: (1) Each decision maker is asked to express his/her preference in relation to an additive value model with incomplete preference statements. (2) A range-typed representation method for utility is used. The range-typed utility representation makes it easy to compare each group members utility information with a groups one and to aggregate each group members utility information into a groups one. Utility range is calculated from each group members incomplete information. (3) An interactive procedure is provided to help the group reach a consensus. It helps each group member to modify or complete his/her utility with ease compared to groups utility range. (4) We formally describe theoretic models for establishing groups pairwise dominance relations with groups utility range by using a separable linear programming technique.

Mining the change of customer behavior in an internet shopping mall

Abstract Understanding and adapting to changes of customer behavior is an important aspect for a internet-based company to survive in a continuously changing environment. The aim of this paper is to develop a methodology which detects changes of customer behavior automatically from customer profiles and sales data at different time snapshots. For this purpose, we first define the three types of changes as emerging pattern, unexpected change and the added/perished rule, then, we develop similarity and difference measures for rule matching to detect all types of change. Finally, the degree of change is evaluated to detect significantly changed rules. Our proposed methodology can evaluate the degree of changes as well as detect all kinds of change automatically from different time snapshot data. A case study on an internet shopping mall for evaluation of this methodology is also provided.

Multi-attribute decision aid under incomplete information and hierarchical structure

Abstract This paper presents methods for dealing with incomplete information about both attribute weights and values under a hierarchically structured value tree. Incomplete information in this paper covers arbitrary linear inequalities and is hence to treat a more general situation than a previous restrictive definition of incomplete information that typically includes interval judgment. This may give decision makers chances that is enhanced freedom of choice and comforts of specification. We propose two techniques for prioritizing alternatives by (strict) dominance relationship. One is the extension of a previous method for operating flat-structured value trees to hierarchical ones. The other approach propagates pairwise dominance values from leaf nodes to topmost node consecutively which is also an extension of a previous method. Because the strict dominance rule fails to fully prioritize alternatives, as is usual the case under incomplete information, we suggest a new method, a measure of preference strength, which can provide decision makers with a single optimal alternative or full rank of alternatives without any further interaction with decision makers.

Modeling a class of decision problems using artificial neural networks

Abstract This paper presents an artificial neural network to build a decision model, together with a discussion about implementation of decision class analysis. In contrast to evaluating or analyzing decision problems, there has been little research to build decision models such as the influence diagram. In practice, generating an influence diagram requires much time and effort. Furthermore, the resulting model can be generally applicable to only a specific decision problem. In order to reduce the burden of modeling decision problems, the concept of decision class analysis (DCA) is proposed. DCA treats a set of decision problems having some degree of similarity as a single unit. This paper presents a scheme within which a neural network is used to implement DCA, i.e. to model similar decision problems within the same class. An influence diagram model is used to represent the decision problem. It is a good tool for knowledge representation of complex decision problems under uncertainty. After the influence diagram is briefly described and the concept of DCA is introduced, we propose a method for developing influence diagrams using a feedforward neural net. We also present the results of neural net simulation with an example of a class of decision problems.

Neural network-based decision class analysis for building topological-level influence diagram

In order to reduce the burden of modeling decision problems, the concept of decision class analysis (DCA) was proposed. DCA treats a set of decision problems having some degree of similarity as a single unit. This paper presents a scheme within which a neural network is used to implement DCA. An influence diagram model is employed to represent the decision problem, since the diagram is a good tool for knowledge representation of complex decision problems under uncertainty. DCA under consideration is viewed as a classification problem where a set of input?output data pairs is given. We thus utilize a feed-forward neural network with a supervised learning procedure so as to develop DCA and then to generate an influence diagram in the topological level. This paper also presents the results of the neural net simulation with an example of a class of decision problems.

Development of a case‐based decision support system for process modeling in BPR

This paper presents a knowledge-based methodology for business process reengineering that uses a case-based reasoning paradigm to provide decision support to its users in the modeling of a current problem and a redesign of critical business processes. As a process modeling tool for representing the business process, the event process chain (EPC) modeling method is used in this paper. We developed a CAPMOSS (CAse-based Process MOdeling Supporting System) to support our proposed methodology. To reengineer a new business process problem, CAPMOSS retrieves from its case base the case that is most similar to the current problem. CAPMOSS uses a retrieved case to guide the structuring of AS-IS models and TO-BE models of a target business process. Using the transformational knowledge of a retrieved case, CAPMOSS helps the user to transform an AS-IS model into a TO-BE model for the target process with ease and the purchasing process in a government R&D institute is explained as an application of this approach. Copyright

A methodology for modeling influence diagrams: a case-based reasoning approach

In this paper, a case-based reasoning approach to build an influence diagram is described. Building an influence diagram in decision analysis is known to be a most complicated and burdensome process. To overcome such a difficulty, decision class analysis is suggested, which treats a set of decisions having some degree of similarity as a single unit. This research suggests a case-based reasoning approach as a methodology to analyze a class of decisions. The candidate influence diagrams are retrieved from a set of similar influence diagrams, a case base. They are combined and modified by the node classification tree and DM’s preference for the given decision problem. For such a purpose, the case representation and retrieval process is explained with the adaptation process. We suggest using two measure, the fitness and garbage ratio for the case retrieval process. The basic concept of decision class analysis and case-based reasoning is very similar so the case-based reasoning approach is believed to be a better methodology to implement a decision class analysis. Copyright

A Network Analysis of Information Exchange using Social Media in ICT Exhibition

The proliferation of using social media and social networking services affects the lifestyles of people. These phenomena are useful to companies that wish to promote and advertise new products or services through these social media; these social media venues also come with large amounts of user data. However, studies that analyze the data of social media within the perspective of information exchanges are hard to find. Much of the previous research in this area is focused on measuring the performance of exhibitions using general statistical approaches and piecemeal measures. Therefore, in this study, we want to analyze the characteristics of information exchanges in social media by using Twitter data sets, which are relating to the Mobile World Congress (MWC). Using this methodology provides exhibition organizers and exhibitors to objectively estimate the effect of social media, and establish strategies with social media use. Through a user network analysis, we additionally found that social attributes are as important as the popular attribute regarding the sustainability of information exchanges. Consequently, this research provides a network analysis using the data derived from the use of social media to communicate information regarding the MWC exhibition, and reveals the significance of social attributes such as the degree and the betweenness centrality regarding the sustainability of information exchanges.

Emotion Detection Model based on Sequential Neural Networks in Smar t Exhibition Environment

In the various kinds of intelligent services, many studies for detecting emotion are in progress. Particularly, studies on emotion recognition at the particular time have been conducted in order to provide personalized experiences to the audience in the field of exhibition though facial expressions change as time passes. So, the aim of this paper is to build a model to predict the audience’s emotion from the changes of facial expressions while watching an exhibit. The proposed model is based on both sequential neural network and the Valence-Arousal model. To validate the usefulness of the proposed model, we performed an experiment to compare the proposed model with the standard neural-network–based model to compare their performance. The results confirmed that the proposed model considering time sequence had better prediction accuracy.