• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data (a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology fur sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data(a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology for sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

• 한국환경교육학회지:환경교육
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• 제20권1호
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• pp.90-105
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• 2007

The purpose of this study is to find the influence of environmental issue analysis instruction on the environmental decision making ability for grade 5 elementary school students. The study was done through pre and post testing control group structure. The object of this study is grade 5 of I elementary school students which were divided into 35 student test group and 54 student control group. Through studying references, the selection standard of appropriate environment issue and the environmental issue analysis instructing objective. Conducted the environment issue instructing based on the selected environment issue and instructing objective. The classes were held in total of 6 sessions in the chapters related to class objective and class content within the curriculum. The pre and post testing was done using environment decision making ability test sheet which was reconstructed by myself and the results were analyzed by t-test. As a result of comparing pre and post testing the students in test group showed significant results in the processes of problem recognition, evaluation of alternatives, behave planing (p<.001). As a result of comparing the differences of environment decision making ability of pre and post test of test group and control group, it showed significant results in the process of evaluation of alternatives(p<.00l). The environment issue analysis class has positive influence on the environment decision making abilities of the students but since the outcome of environment decision making ability is lower, there is a need for long term environment education plan and further studies to find whether the environment issues within the textbook is appropriate in the elementary student level, useful school aspect and the influence of environment issue analysis class on the change of values for individuals.

• 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.

• 시큐리티연구
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• 제21호
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• pp.19-33
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• 2009

본 연구는 경호학을 전공하는 대학생을 대상으로 전공수업만족도가 진로 결정 효능감에 미치는 영향에 대해 알아보는데 있으며, 다음과 같은 결론을 얻었다. 첫째, 성별에 따른 전공수업만족도와 진로 결정 효능감의 차이에서는 전공수업만족도의 하위변인 중 학습요인, 대인관계, 적성요인, 진로요인, 진로 결정 효능감의 하위변인 중 미래계획, 목표해결, 직업정보, 문제해결에서 통계적으로 유의미한 차이가 없는 것으로 나타났다. 둘째, 소속 학교와 학년에 따른 전공수업만족도와 진로 결정 효능감의 차이에서는 전공수업 만족도의 하위변인 중 학습요인, 대인관계, 적성요인에서 통계적으로 유의미한 차이가 있는 것으로 나타났다. 셋째, 전공수업만족이 미래계획에 미치는 영향을 분석한 결과 전공수업만족도의 학습요인, 적성요인, 진로요인이 통계적으로 유의미한 영향을 미치는 것으로 나타났다. 넷째, 전공수업만족이 목표해결에 미치는 영향을 분석한 결과 전공수업만족도의 학습요인, 적성요인이 목표해결에 통계적으로 유의미한 영향을 미치는 것으로 나타났다. 다섯째, 전공수업만족이 직업정보에 미치는 영향을 분석한 결과 전공수업만족도의 적성요인, 진로요인이 직업정보에 통계적으로 유의미한 영향을 미치는 것으로 나타났다. 여섯째, 전공수업만족이 문제해결에 미치는 영향을 분석한 결과 전공수업만족도의 진로요인이 문제해결에 통계적으로 유의미한 영향을 미치는 것으로 나타났다.

• 한국조리학회지
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• 제14권4호
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• pp.257-268
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• 2008

The purpose of this study was to conduct factor analysis on Foodservice consumers' lifestyle patterns in dietary life, classify lifestyle patterns, and identify how lifestyle patterns influence the 5 stages of decision making process for purchase. Among 300 copies of the distributed questionnaire, 259 copies were collected for this study. It used a total of 283 copies as the effective samples for empirical analysis except 12 copies with false entries among them. For data analysis, it conducted frequency analysis, validity and reliability analysis, factor analysis, and multiple regression analysis using SPSS 12.0. As a result, Hypothesis 1 was significant while Hypothesis 2, 3, and 5 were partially significant. On the contrary, Hypothesis 4 was not significant. Therefore, lifestyle patterns had partially significant effects on decision making process for dining-out purchase. This study subdivided dining-out consumers' lifestyles which were limited to dietary life, and also subdivided decision making process for dining-out consumers' dining-out purchase into five stages. It is significant and very suggestive that this study identified what lifestyle patterns concretely had significant effects on the specific decision making stage. In the future, the researches on adolescent class and silver class should be executed continuously.

• 한국인공지능학회지
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• 제7권2호
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• pp.19-24
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• 2019

Lung cancer is a chronic disease which ranks fourth in cancer incidence with 11 percent of the total cancer incidence in Korea. To deal with such issues, there is an active study on the usefulness and utilization of the Clinical Decision Support System (CDSS) which utilizes machine learning. Thus, this study reviews existing studies on artificial intelligence technology that can be used in determining the lung cancer, and conducted a study on the applicability of machine learning in determination of the lung cancer by comparison and analysis using Azure ML provided by Microsoft. The results of this study show different predictions yielded by three algorithms: Support Vector Machine (SVM), Two-Class Support Decision Jungle and Multiclass Decision Jungle. This study has its limitations in the size of the Big data used in Machine Learning. Although the data provided by Kaggle is the most suitable one for this study, it is assumed that there is a limit in learning the data fully due to the lack of absolute figures. Therefore, it is claimed that if the agency's cooperation in the subsequent research is used to compare and analyze various kinds of algorithms other than those used in this study, a more accurate screening machine for lung cancer could be created.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 추계학술대회-지능형 정보기술과 미래조직 Information Technology and Future Organization
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• pp.355-361
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• 1999

An application of case-based reasoning is proposed to build an influence diagram for identifying successful new ventures. The decision to invest in new ventures in characterized by incomplete information and uncertainty, where some measures of firm performance are quantitative, while some others are substituted by qualitative indicators. Influence diagrams are used as a model for representing investment decision problems based on incomplete and uncertain information from a variety of sources. The building of influence diagrams needs much time and efforts and the resulting model such as a decision model is applicable to only one specific problem. However, some prior knowledge from the experience to build decision model can be utilized to resolve other similar decision problems. The basic idea of case-based reasoning is that humans reuse the problem solving experience to solve a new decision. In this paper, we suggest a case-based reasoning approach to build an influence diagram for the class of investment decision problems. This is composed of a retrieval procedure and an adaptation procedure. The retrieval procedure use two suggested measures, the fitting ratio and the garbage ratio. An adaptation procedure is based on a decision-analytic knowledge and decision participants knowledge. Each step of procedure is explained step by step, and it is applied to the investment decision problem in new ventures.

• Journal of the Korean Data and Information Science Society
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• 제23권6호
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• pp.1137-1144
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• 2012

요즘 대부분의 대학들이 국제화를 목표로 재학생들의 어학능력 및 국제적 경쟁력 강화와 함께 영어강의의 중요성이 증대되고 있다. 본 연구에서는 강의평가점수를 이용해서 과목특성 변수들과 개인특성 변수들 별로 영어강의와 한국어강의의 효과성을 비교하였다. 또한 로지스틱회귀분석과 의사결정나무분석을 이용하여 어떤 요인들이 영어강의가 한국어강의에 비해서 효과적인지를 주효과와 상호작용효과 측면에서 분석하였다. 분석결과에 따르면 영어강의의 효과성에 영향을 미치는 변수로는 학년, 계열, 강좌규모, 평균평점, 계열, 전형방법 등으로 나타났다. 또한 영어강의 효과성이 특히 높은 그룹은 1학년이면서 인문계열인 경우, 그리고 1학년이면서 자연 및 예체능계열이고 평균평점이 높은 그룹이 높게 나타났다. 그리고 영어강의의 효과성 비율이 특히 낮은 그룹은 2-3학년 학생이면서 강좌규모가 크고 인문계열인 경우로 나타났다.

• Journal of Korea Artificial Intelligence Association
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• 제1권1호
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• pp.1-6
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• 2023

This study developed models using decision forest, support vector machine, and logistic regression methods to predict and prevent suicidal ideation among Korean adolescents. The study sample consisted of 51,407 individuals after removing missing data from the raw data of the 18th (2022) Youth Health Behavior Survey conducted by the Korea Centers for Disease Control and Prevention. Analysis was performed using the MS Azure program with Two-Class Decision Forest, Two-Class Support Vector Machine, and Two-Class Logistic Regression. The results of the study showed that the decision forest model achieved an accuracy of 84.8% and an F1-score of 36.7%. The support vector machine model achieved an accuracy of 86.3% and an F1-score of 24.5%. The logistic regression model achieved an accuracy of 87.2% and an F1-score of 40.1%. Applying the logistic regression model with SMOTE to address data imbalance resulted in an accuracy of 81.7% and an F1-score of 57.7%. Although the accuracy slightly decreased, the recall, precision, and F1-score improved, demonstrating excellent performance. These findings have significant implications for the development of prediction models for suicidal ideation among Korean adolescents and can contribute to the prevention and improvement of youth suicide.