• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2003년도 춘계학술대회 논문집
• /
• pp.22-25
• /
• 2003

It is usually difficult for a single user to have all the essential knowledge on various Rapid Prototyping processes and techniques. It is therefore necessary to capture knowledge and experience of users of expert level into a decision-support system which provides quicker and more interactive way to select proper RP process and/or machine. rather than reading reports on benchmarking studies and comparing tables and graphs. In this paper two algorithms are presented, which may be used in such a decision-support system. together with its applications. The one is an extended PRES(Project Evaluation and Selection) algorithm which applies weighting factors of each attribute. The other is a LCE(Linear Confidence Equation) algorithm which is proposed to apply user's input requirements as well as weighting factors.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2005년도 공동추계학술대회
• /
• pp.509-515
• /
• 2005

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved it more powerful than traditional artificial neural networks (ANNs)(Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al, 2005; Kim, 2003). The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is cost-sensitive. Therefore, it is necessary to convert the output of the classifier into well-calibrated posterior probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create probabilities (Platt, 1999; Drish, 2001). This study applies a method to estimate the probability of outputs of SVM to bankruptcy prediction and then suggests credit scoring methods using the estimated probability for bank's loan decision making.

• 한국전자통신학회논문지
• /
• 제14권4호
• /
• pp.775-780
• /
• 2019

미래를 예측하는 기법은 통계에 기반을 둔 것과 딥러닝에 기반을 둔 기술로 분류할 수 있다. 그중 통계에 기반을 둔 것이 간단하고 정확성이 높아서 많이 사용된다. 하지만 실무자들은 많은 분석기법의 올바른 사용에 어려움이 많다. 이번 연구에서는 마케팅에 관련된 데이터에 다항로지스틱회귀, 의사결정나무, 랜덤포레스트, 서포트벡터머신, 베이지안 추론을 적용하여 예측의 정확성을 비교하였다. 동일한 마케팅 데이터를 대상으로 하였고, R을 활용하여 분석을 진행하였다. 마케팅 분야의 데이터 특성을 반영한 다양한 기법의 예측 결과가 실무자들에게 좋은 참고가 될 것으로 생각한다.

• ETRI Journal
• /
• 제39권5호
• /
• pp.621-631
• /
• 2017

Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)-based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false-negative rate. This article proposes a new imbalanced SVM (termed ImSVM)-based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. ImSVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GMean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate ImSVM. Highly imbalanced training sample sets are constructed. The experimental results show that ImSVM outperforms over-sampling techniques and several existing imbalanced SVM-based techniques.

• 디지털융복합연구
• /
• 제11권4호
• /
• pp.251-258
• /
• 2013

21세기 들어와 기업은 갈수록 복잡해지고 변동성이 커지는 경영환경에 직면하게 되었다. 또한 다양한 고객의 요구와 치열해지는 경쟁에 대응하기 위하여 경영 전 분야에 걸쳐 긴밀하고 체계적인 의사결정 대안을 선택하고 관리해야 하는 부담이 가중되고 있다. 본 연구에서는 경영의사결정을 지원할 수 있는 기술모델을 컨설팅 문제해결 절차와 기법, 경영프로세스 지식체계와 연계하여 구성하고 문제해결도구로써 지식체계에 기반한 시뮬레이션 도구 활용에 대하여 논의하였다. 나아가 제안하는 경영의사결정지원 기술모델의 시스템구현을 통한 체계적인 지식체계의 확충과 발전 필요성을 논의하였다. 경영의사결정지원 기술모델은 크게 세 가지 요소로써 구성되는데 첫 번째는 문제해결기법으로 참조자료로써 활용이 되며, 두 번째는 표준비즈니스 프로세스와 참조프로세스 모델 정보를 포함하는 프로세스관련 지식체계이다. 세 번째 요소는 문제해결기법과 프로세스 관련 지식체계를 정보로 활용하여 대안을 생성하고 분석하는 도구인 시뮬레이터로 정의하였다. 위의 세 가지 주요요소들은 컨설팅 과정전반에서 표준화된 문세해결 절차에 따라 체계적 분석을 수행하도록 하는 가이드라인을 제세하고 각 분석단계별로 분석기법에 대한 정보를 제공하여 의사결정의 정확성와 객관성 확보를 지원한다. 경영의사결정 지원기술 모델은 궁극적으로 지식집약형 컨설팅 프로세스를 지원하여 다양한 컨설팅 지식을 축적하고 컨설팅기법의 발전과 활용을 촉진하여 컨설팅 산업 발전의 기반기술 개발에 기본 프레임워크를 제공하는데 의의를 지닌다.

• 한국국방경영분석학회지
• /
• 제27권2호
• /
• pp.37-55
• /
• 2001

The main purpose of this study is to review the current system and to develop a decision support system for evaluating the priorities among those possible alternatives in the army facility enterprise. This paper also provides an information system which can be effectively applied to various criteria and stages in decision making process such as Planning and Programming phases in PPBEES. The model base of decision support systems uses the concepts of the analytic hierarchy process along with the supplementary techniques such as TOPSIS and 0-1 integer programming. Both AHP and TOPSIS are used scoring approaches in the Planning phase and IP is induced at the Programming phase to give GO/NO-GO solution for each project. We use Expert Choice, Excel and LINDO s/w's to implement a prototyped model. The proposed methodology in this paper enables the decision makers to evaluate the priority based on quantitative and qualitative data in a systematic way.

• 임상간호연구
• /
• 제24권1호
• /
• pp.44-55
• /
• 2018

Purpose: This study aimed to explore and understand the experience of decision making among women undergoing or forgoing selective fetal reduction who have higher-order multiple pregnancies through assisted reproductive techniques. Methods: A qualitative study was conducted from August 1, to October 30, 2013. Eight participants were interviewed and the interviews were audio-recorded and transcribed verbatim. Six persons participated in in-depth interviews in person and two participated over the telephone. A thematic analysis was conducted. Results: Four themes were identified and carefully named: Confusion after higher-order multiple pregnancy; Obstacles to choice: Uncertain safety; Weighing between reality and ideality and; Influences of medical professionals. Conclusion: The results demonstrated a wide range of factors considered by women when making decisions about selective fetal reduction, and mothers' feelings of conflict and distress in the decision-making process. The results suggest that it is important for nurses to provide emotional support and consolation, in addition to sufficient information. These findings will help nurses improve their counseling techniques by understanding the situation of infertile couples.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2000년도 추계정기학술대회:지능형기술과 CRM
• /
• pp.369-374
• /
• 2000

Despite their tilde application of some traditional project management techniques like the Program Evaluation and Review Technique, they lack of learning, one of important factors in many disciplines today due to a static view far prefect progression. This study proposes a framework for estimation by learning based on a Linear Bayesian approach. As a project progresses, we sequentially observe the durations of completed activities. By reflecting this newly available information to update the distribution of remaining activity durations and thus project duration, we can implement a decision support system that updates e.g. the expected project completion time as well as the probabilities of completing the project within talc due date and by a certain date. By Implementing such customized systems, project manager can be aware of changing project status more effectively and better revise resource allocation plans.

• 지능정보연구
• /
• 제6권2호
• /
• pp.91-98
• /
• 2000

Despite their wide application of some traditional project management techniques like the Program Evaluation and Review Technique, they lack of learning, one of important factors in many disciplines today, due to a static view for project progression. This study proposes a framework for estimation by loaming based on a Linear Bayesian approach. As a project Progresses, we sequentially observe the durations of completed activities. By reflecting this newly available information to update the distribution of remaining activity durations and thus project duration, we can implement a decision support system that updates e.g., the expected project completion time as well as the probabilities of completing the project within the due bate and by a certain date. By implementing such customized system, project manager can be aware of changing project status more effectively and better revise resource allocation plans.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2006년도 춘계학술대회
• /
• pp.122-129
• /
• 2006

The context-awareness has become the one of core technologies and the indispensable function. for application services in ubiquitous computing environment. In this research, we incorporated the capability of context-awareness in a music recommendation system. Our proposed system consists of such components as Intention Module, Mood Module and Recommendation Module. Among these modules, the Intention Module infers whether a user wants to listen to the music or not from the environmental context information. We built the Intention Module using data mining techniques such as decision tree, support vector machine and case-based reasoning. The results showed that the case-based reasoning model outperformed the other models and its accuracy was 84.1%.