• The Journal of Engineering Geology
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• v.18 no.1
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• pp.45-54
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• 2008

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in gneiss area, a prediction technique was developed by the use of a decision tree model, which is one of the statistical analysis methods. The slope hazards data of Seoul and Kyonggi Province, which were induced by heavy rainfall in 1998, were 104 sections in gneiss area. The number of data applied in developing prediction model was 61 sections except a vacant value. Among these data, the number of data occurred slope hazards was 34 sections and the number of data non-occurred slope hazards was 27 sections. The statistical analyses using the decision tree model were applied to chi-square statistics, gini index and entrophy index. As the results of analyses, a slope angle, a degree of saturation and an elevation were selected as the classification standard. The prediction model of decision tree using entrophy index is most likely accurate. The classification standard of the selected prediction model is composed of the slope angle, the degree of saturation and the elevation from the first choice stage. The classification standard values of the slope angle, the degree of saturation and elevation are $17.9^{\circ}$, 52.1% and 320 m, respectively.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.239-242
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• 2007

최근 데이터의 양이 많아지고 다양해짐에 따라서 데이터를 활용하기 위한 데이터 마이닝에 관한 관심이 중대되고 있다. 데이터 분석을 위한 수집 데이터에는 수집 과정에서 분석가가 원치 않은 데이터 잡음이 발생하는 경우가 있고 그 데이터가 다른 데이터들과 같은 가중치로 데이터 마이닝에 반영되는 경우 예상과 다른 결과를 얻을 수 있다. 따라서 데이터 분석 시 데이터와 전문가 의견이 고려된 데이터 엔트로피(Entropy)를 사용하여 잡음 데이터를 다를 필요가 있다. 본 논문에서는 전문가의견을 이용한 전문가 의견 목록을 만들고 이를 데이터와 비교하여 유사한 정도에 따라 각 데이터에 가중치를 부여한다. 그리고 이 데이터를 활용한 의사결정나무(Decision Tree)를 사용하여 기존 데이터를 이용한 의사결정나무 보다 데이터 잡음의 영향을 줄이는 방법을 제안한다. 제안한 방법은 학습자의 학습 활동에서 수집된 학습 행위 데이터를 사용하여 실험하였다.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.179-185
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• 2015

The cost of advanced medical technologies is commonly considered to be a major factor in the overall escalation of expenditures on health. The use of computed tomography (CT) scanning has increased dramatically over the past decade. CT has been rapidly adopted, despite their high cost. The aim of this study is to analysis the increasing factor of the frequency of the CT, using the decision tree model. Finally, we propose the effective policy option of diagnostic imaging technology in Korea.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.179-189
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• 2018

Currently, research on smart factories is steadily being carried out in terms of implementation strategies and considerations in construction. Various studies have not been conducted on companies that introduced smart factories. This study conducted a questionnaire survey for SMEs applying the basic stage of smart factory. And the cluster analysis was conducted to examine the characteristics of the company. In addition, we conducted Decision Tree and Naive Bay to examine how the characteristics of a company are derived and compare the results. As a result of the cluster analysis, it was confirmed that the group was divided into the high satisfaction group and the low satisfaction group. The decision tree and the Naive Bay analysis showed that the higher satisfaction group has high productivity.

• Korean Journal of Construction Engineering and Management
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• v.22 no.6
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• pp.76-86
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• 2021

Construction projects have high potentials of claims and disputes due to inherent risks where a variety of stakeholders are involved. Since disputes could cause losses in terms of cost and time, it is a critical issue for contractors to forecast and pro-actively manage disputes in advance in order to secure project efficiency and higher profits. The objective of the study is to develop a decision tree-based predictive analytics model for forecasting dispute types and their probabilities according to construction project conditions. It can be a useful tool to forecast potential disputes and thus provide opportunities for proactive management.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.139-155
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• 2003

This paper presents the new methodology of analyzing and classifying patterns of customers in mobile telecommunication market to enhance the performance of predicting the credit information based on the decision tree and neural network. With the application of variance selection process from decision tree, the systemic process of defining input vector's value and the rule generation were developed. In point of customer management, this research analyzes current customers and produces the patterns of them so that the company can maintain good customer relationship and makes special management on the customer who has huh potential of getting out of contract in advance. The real implementation of proposed method shows that the predicted accuracy is higher than existing methods such as decision tree(CART, C4.5), regression, neural network and combined model(CART and NN).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.3
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• pp.518-523
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• 2006

Due to keen competition among companies, they have segmented customers and they are trying to offer specially targeted customer by means of the distinguished method. In accordance, data mining techniques are noted as the effective method that extracts useful information. This paper explores customer segmentation of the home study company using a hybrid decision tree and artificial neural network model. With the application of variance selection process from decision tree, the systemic process of defining input vector's value and the rule generation were developed. In point of customer management, this research analyzes current customers and produces the patterns of them so that the company can maintain good customer relationship. The case study shows that the predicted accuracy of the proposed model is higher than those of regression, decision tree (CART), artificial neural networks.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.993-1003
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• 2012

The purpose of this study was to estimate the determinants of victory and defeat through analyzing records of Korean men pro-basketball. Statistical models of victory and defeat were established by collecting present basketball records (2010-2011, 2011-2012 season). Korea Basketball League (KBL) informs records of every pro-basketball game data. The six offence variables (2P%, 3P%, FT%, OR, AS, TO), and the four defense variables (DR, ST, GD, BS) were used in this study. PASW program was used for logistic regression and Answer Tree program was used for the decision tree. All significance levels were set at .05. Major results were as follows. In the logistic regression, 2P%, 3P%, and TO were three offense variables significantly affecting victory and defeat, and DR, ST, and BS were three significant defense variables. Offensive variables 2P%, 3P%, TO, and AS are used in constructing the decision tree. The highest percentage of victory was 80.85% when 2P% was in 51%-58%, 3P% was more than 31 percent, and TO was less than 11 times. In the decision tree of the defence variables, the highest percentage of victory was 94.12% when DR was more than 24, ST was more than six, and BS was more than two times.

• Journal of Digital Convergence
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• v.13 no.1
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• pp.353-366
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• 2015

The purpose of this study was to examine how region-specific characteristics affect the occurrence of cardiac arrest. To analyze, we combined a unique data set including key indicators of health condition and cardiac arrest occurrence at the 244 small administrative districts. Our data came from two main sources in Korea Center For Disease Control and Prevention (KCDC): 2010 Out-of-Hospital Cardiac Arrest Surveillance and Community Health Survey. We analyzed data by using multiple regression, geographically weighted regression and decision tree. Decision tree model is selected as the final model to explain regional variations of cardiac arrest. Factors of regional variations of cardiac arrest occurrence are population density, diagnosis rates of hypertension, stress level, participating screening level, high drinking rate, and smoking rate. Taken as a whole, accounting for geographical variations of health conditions, health behaviors and other socioeconomic factors are important when regionally customized health policy is implemented to decrease the cardiac arrest occurrence.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.726-730
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• 2007

우리나라는 6월부터 9월까지의 우기에 강우가 집중 발생하는 기상특성으로 인해 자연재해의 95% 이상이 집중호우와 태풍에 의한 풍수해로 집계되고 있을 만큼 홍수피해에 취약하며, 오래전부터 홍수방어에 대한 구조적 대책이 시행되어왔다. 본 연구의 목적은 의사결정기법인 Decision Tree(의사결정나무)를 활용하여 유역종합치수계획의 구조적 홍수방어 최적대안 선정을 위한 후보대안들을 제시하여 홍수저감능력을 효율적으로 극대화 하는데 그 목적이 있다. 본 연구는 유역이 가지고 있는 치수적 기능을 최대한 살리고 상 하류의 유기적인 방어 기능을 도모하고자 하였으며, 또한 도시유역 홍수방어 대안 조합 지침을 마련하여 실무에 적용가능한 안을 제시하였다.