• International Journal of Advanced Culture Technology
• /
• v.10 no.2
• /
• pp.28-33
• /
• 2022

This study aims to investigate factors that affect elderly poverty based on a comprehensive and universal perspective, suggesting some alternatives for improving the poverty rate of the elderly. The comprehensive and universal approach to the poverty of the aged that this study attempts can give a better understanding of the elderly poverty beyond the contribution of the existing literature, with the research model including individual, family, labor, and income factors as the causes of old-age poverty from the comprehensive and universal perspective on the causes of poverty of the elderly. In addition, the study attempts to input variants of variables into the equation for the causes of elderly poverty by using panel data from the 8th Korean Retirement and Income Study. This study employs decision tree analysis to determine the cause of the poverty of the elderly using CHAID. The decision tree analysis shows that the most vital variable affecting elderly poverty is making income. For the poor elderly without earned income, public pensions, educational careers, and residential areas influence elderly poverty, but for the poor elderly with earned income, wage earners and gender are variables that affect poverty. This study suggests some alternatives to improve the poverty rate of the aged. The government should create a better working environment such as senior re-employment for old people to be able to participate in economic activities, improve public pension or social security for workers with unfavorable conditions for public security of old age, and give companies that create employment of the aged diverse incentives.

• Journal of Environmental Impact Assessment
• /
• v.20 no.5
• /
• pp.663-673
• /
• 2011

In the face of accelerating biodiversity loss and its significance in our coexistence with nature, biodiversity is becoming more crucial in sustainable development perspective. To estimate biodiversity in the future which provides valuable information for decision making system especially in the national level, a quantitative approach must be studied forehand as a baseline of the present status. In this study, we developed a large-scale map of Plant Species Richness (PSR, typical indicator of biodiversity) for Young-dong and Pyung-chang provinces. Due to the accessibility of appropriate data and advance of modelling techniques, reduction of variables without deteriorating the predictive power is considered by applying Genetic algorithm. In addition, a number of Correctly Classified Instances (CCI) with 10-fold cross validation which indicates the predictive power, was carried out for evaluation. This study, as a fundamental baseline, will be beneficial in future land work as well as ecosystem restoration business or other relevant decision making agenda.

• Journal of Engineering Education Research
• /
• v.13 no.4
• /
• pp.51-59
• /
• 2010

In order to manage the lectures efficiently in the university and improve the educational outcome, the process is needed that make diagnosis of the present educational outcome of each classes on a lecture and find factors of educational outcome. In most studies for finding the factors of the efficient lecture, statistical methods such as association analysis, regression analysis are used usually, and recently decision tree analysis is employed, too. The decision tree analysis have the merits that is easy to understand a result model, and to be easy to apply for the decision making, but have the weaknesses that is not strong for characteristic of input data such as multicollinearity. This paper indicates the weaknesses of decision tree analysis, and suggests the experimental solution using multiple decision tree algorithm to supplement these problems. The experimental result shows that the suggested method is more effective in finding the reliable factors of the educational outcome.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2006.05a
• /
• pp.554-557
• /
• 2006

For various historical and technical reasons, the safety of dams has been controlled by an engineering standards-based approach, which has been developed over many years, initially for the design of new dams, but increasingly applied over the past few decades to assess the safety of existing dams. And some countries were asked for risk assessment on existing dam, which included structural, hydraulic safety of dam and social risk. Whereas other countries have developed and adapted as an additional tool to assist in decision-making for dam safety management. Dam risk analysis should need the reliability data of dam failures, the past constructed history and management records of existing dam. It is thought with risk analysis method of dams for structural safety management in domestic that suitable to use consider an event tree, fault tree and conditioning indexes method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.10a
• /
• pp.170-174
• /
• 1997

This paper studies a method for making more efficient classification rules in the ID3 using the rough set theory. Decision tree technique of the ID3 always uses all the attributes in a table of examples for making a new decision tree, but rough set technique can in advance eleminate dispensable attributes. And the former generates only one type of classification rules, but the latter generates all the possibles types of them. The rules generated by the rough set technique are the simplist from as proved by the rough set theory. Therefore, ID3, applying the rough set technique, can reduct the size of the table of examples, generate the simplist form of the classification rules, and also implement an effectie classification system.

• Journal of the Korea Concrete Institute
• /
• v.14 no.6
• /
• pp.1022-1031
• /
• 2002

This paper presents efficient models for bridge structures using CART-ANFIS (classification and regression tree-adaptive neuro fuzzy inference system). A fuzzy decision tree partitions the input space of a data set into mutually exclusive regions, each region is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it continuous and smooth everywhere. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.17 no.2
• /
• pp.131-140
• /
• 2004

It really needs fuzzy decision making of integrity assessment considering about both durability and load carrying capacity for maintenance and administration, such as repairing and reinforcing. This thesis shows efficient models about reinforced concrete structure using CART-ANFIS. It compares and analyzes decision trees parts of expert system, using the theory of fuzzy, and applying damage & diagnosis at reinforced concrete structure and decision trees of integrity assessment using established artificial neural. Decided the theory of reinforcement design for recovery of durability at damaged concrete ＆ the theory of reinforcement design for increasing load carrying capacity keep stability of damage and detection. It is more efficient maintenance and administration at reinforced concrete for using integrity assessment model of this study and can carry out predicting cost of life cycle.

• Journal of the Korea Society for Simulation
• /
• v.32 no.3
• /
• pp.67-74
• /
• 2023

A semiconductor fabrication facility(FAB) is one of the most capital-intensive and large-scale manufacturing systems which operate under complex and uncertain constraints through hundreds of fabrication steps. To improve fab performance with intuitive scheduling, practitioners have used weighted-sum scheduling. Since the determination of weights in the scheduling significantly affects fab performance, they often rely on simulation-based decision making for obtaining optimal weights. However, a large-scale and high-fidelity simulation generally is time-intensive to evaluate with an exhaustive search. In this study, we investigated three sampling methods (i.e., Optimal latin hypercube sampling(OLHS), Genetic algorithm(GA), and Decision tree based sequential search(DSS)) for the optimization. Our simulation experiments demonstrate that: (1) three methods outperform greedy heuristics in performance metrics; (2) GA and DSS can be promising tools to accelerate the decision-making process.

• Journal of Information Management
• /
• v.19 no.1
• /
• pp.1-29
• /
• 1988

The purpose of this study is to investigate a decision making support system for the effective information management. Decision making theory is reviewed and problems are discussed. A model is suggested through the computing of expected monetary value in decision tree technique. The expected monetary value is computed by 1 inking the probability theory with chance node. The selection of right expected monetary value and expected value of perfect information will make great advance the present system. It is concluded that expected monetary value and expected value of perfect information in decision tree techniques will make great aids to advance information management system.

• Journal of the Korea Society for Simulation
• /
• v.23 no.4
• /
• pp.189-195
• /
• 2014

In this paper, we conducted a study on place-free and time-free cloud computing (CC) adoption decision-making model. Panel survey data which is collected from 65 people and CART (classification and regression tree) which is one of data mining approaches are used to construct decision-making model. In this modeling, there are 2 steps: In the first step, significant questions (variables) are selected. After that, the CART decision-making model is constructed using the selected variables. In the variable selection stage, the 25 questions are reduced to 5 ones. The benefits of question reduction are quick response from respondent and reducing model-construction time.