• Asia pacific journal of information systems
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• 제7권1호
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• pp.99-111
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• 1997

This paper presents an application of an artificial neural net to the implementation of decision class analysis (DCA), together with the generation of a decision model influence diagram. The diagram is well-known as a good tool for knowledge representation of complex decision problems. Generating influence diagram model is known to in practice require much time and effort, and 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 DCA is introduced. DCA treats a set of decision problems having some degree of similarityz as a single unit. We propose a method utilizing a feedforward neural net with supervised learning rule to develop DCA based on influence diagram, which method consists of two phases: Phase l is to search for relevant chance and value nodes of an individual influence diagram from given decision and specific situations and Phase II elicits arcs among the nodes in the diagram. We also examine the results of neural net simulation with an example of a class of decision problems.

• 경영과학
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• 제27권1호
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• pp.33-43
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• 2010

This paper presents a method for the estimation and analysis of the economic value of weather forecasts for CRM decision-making problems in the leisure industry. Value is calculated in terms of the customer's satisfaction returned from the user's decision under the specific payoff structure, which is itself represented by a customer's satisfaction ratio model. The decision is assessed by a modified cost-loss model to consider the customer's satisfaction instead of the loss or cost. Site-specific probability and deterministic forecasts, each of which is provided in Korea and China, are applied to generate and analyze the optimal decisions. The application results demonstrate that probability forecasts have greater value than deterministic forecasts, provided that the users can locate the optimal decision threshold. This paper also presents the optimal decision strategy for specific customers with a variety of satisfaction patterns.

• 한국농공학회논문집
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• 제54권5호
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• pp.57-67
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• 2012

This paper presents the Multi Criteria Decision Making (MCDM) to evaluate water resources plan for agricultural reservoir. Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) and Analytic Hierarchy Process (AHP) were used to estimate weight and priority of alternatives to find out the most reasonable and efficient way of water resources assessment. The 6 criteria that both decision maker and beneficiary are satisfied have been identified to secure agricultural water resources and then the priority of 10 subcriteria was set. An enhanced PROMETHEE-AHP model was used to perform pairwise comparison and find out the priority of each alternative because the existing decision making model have uncertainty and ambiguity. Comparison analysis of decision making models was carried out to find a way of suitable decision making and validity of PROMETHEE-AHP model was suggested.

• Industrial Engineering and Management Systems
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• 제4권1호
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• pp.82-93
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• 2005

Facility layout problems (FLP) are usually treated as design problems. Lack of systematic and objective tools to compare design alternatives results in decision-making to be dominated by the experiences or preferences of designers or managers. To increase objectivity and effectiveness of decision-making in facility layout selections, a decision support model is necessary. We proposed a decision model, which regards the FLP as a multi-attribute decision making (MADM) problem. We identify sets of attributes crucial to layout selections, quantitative indices for attributes, and methods of ranking alternatives. For a requested facility layout design, many alternatives could be developed. The enormous alternatives, various attributes, and comparison of assigned qualitative values to each attribute, form a complicated decision problem. To treat facility layout selection problems as a MADM problem, we used the linear assignment method to rank before selecting those high ranks as candidates. We modelled the application of the Nemawashi process to simulate the group decision-making procedure and help efficiently achieve agreement. The electronics manufacturing service (EMS) industry has frequent and costly facility layout modifications. Our models are helpful to them. We use an electronics manufacturing service company to illustrate the decision-making process of our models.

• 디지털산업정보학회논문지
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• 제19권1호
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• pp.109-118
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• 2023

Data-based analysis methods have become used more for estimating or predicting housing prices, and neural network models and decision trees in the field of big data are also widely used more and more. Neural network models are often evaluated to be superior to existing statistical models in terms of estimation or prediction accuracy. However, there is ambiguity in determining the input feature of the input layer of the neural network model, that is, the type and number of input features, and decision trees are sometimes used to overcome these disadvantages. In this paper, we evaluate the existing methods of using decision trees and propose the method of using decision trees to prioritize input feature selection in neural network models. This can be a complementary or combined analysis method of the neural network model and decision tree, and the validity was confirmed by applying the proposed method to house price estimation. Through several comparisons, it has been summarized that the selection of appropriate input characteristics according to priority can increase the estimation power of the model.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.150-154
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• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.

• 한국통신학회논문지
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• 제30권8B호
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• pp.562-568
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• 2005

본 논문은 방송통신 융합형 음성서비스라는 신규통신서비스가 시장에 등장했을 경우 어떻게 가격을 설정할 것인지에 관한 내용이다. 우선 가격결정방법 및 결정시스템에 대한 기존 문헌조사를 바탕으로 기 연구된 방법론의 문제점을 도출 한 후 "신서비스 시장진입 가격결정을 위한 기업의 가격설정 의사결정모형 제안하고 있다. 이후 본 논문에서 제시하고 있는 모형에 기반하여 신규 서비스인 방송통신 융합형 음성서비스의 초기 시장진입가격 결정하는 일련의 분석과정을 통하여, 논문에서 제시하고 있는 가격결정모형이 실제 기업에서 충분히 활용할 수 있는 현실적인 의사결정시스템 모형이 될 수 있는 타당성을 검증하고자 한다.

• 한국게임학회 논문지
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• 제11권6호
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• pp.61-70
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• 2011

본 논문에서는 결정트리 학습 알고리즘을 활용한 축구 게임 수비 NPC 제어 방법을 제안한다. 제안하는 방법은 실제 게임 사용자들의 이동 방향 패턴과 행동 패턴을 추출하여 결정트리학습 알고리즘에 적용한다. 그리고 학습된 결정트리를 바탕으로 NPC의 이동방향과 행동을 결정한다. 실험결과 제안하는 방법은 결정트리 학습에 시간이 다소 걸리지만, 학습된 결정트리를 바탕으로 이동방향이나 행동을 결정하는 시간은 약 0.001-0.003 ms(밀리초)가 소요되어 실시간으로 NPC를 제어할 수 있었다. 또한, 제안하는 방법은 현재 상태 정보 뿐만 아니라 이를 분석한 관계정보, 이전 상태 정보도 함께 활용하므로, 기존방법인 (Letia98)에 비해 이동방향 결정시 높은 정확도를 나타냈다.

• 한국유기농업학회지
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• 제26권2호
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• pp.181-202
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• 2018

Most of eco-friendly agrifood processing enterprises in Korean rural area are small and medium-sized business. For this reason, it's hard for eco-friendly agrifood processing enterprises to neither analyze business performance for efficient business management nor establish their own business plan for rational decision-making. Therefore it's necessary to design effective mathematical programming model and to make practical application which can support rational management decision-making ensuring the stable business activity of eco-friendly agrifood processing enterprises. Accordingly this paper focuses on the designing and its application of multi-objective mathematical programming model using goal programming to support rational decision-making of eco-friendly agrifood processing enterprise. Hansalimanseongmachum Food Inc. which runs soy bean processing business making tofu based on regional-based soybean farms around Anseong City will be the specific case to apply multi-objective mathematical programming model in practice. And it will suggest measures to support rational management decision-making of other eco-friendly agrifood processing enterprises.

• 한국게임학회 논문지
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• 제14권4호
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• pp.37-44
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• 2014

본 논문에서는 행동경제학에서 주로 사용되는 전망이론을 게임 인공지능 분야에 도입하여 인간의 인지적 특성을 사실적으로 표현하고자 하는 관점에서 NPC의 의사결정 모델을 제안한다. 이를 위하여 효용 이론의 한계로 지적되었던 기준점 설정의 문제, 민감도 체감성, 손실 회피의 특징을 분석하고, 이를 게임 상에서의 NPC 의사결정 모델에 반영한다. 본 논문에서는 제안 모델을 동적 지형분석에 적용하였으며, 실험을 통해 NPC의 다양한 개성 부과 및 창발적인 행위를 유도할 수 있음을 확인하였다.