• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2C
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• pp.59-69
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• 2009

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in crystalline rocks like gneiss, granite, and so on, a prediction model was developed by the use of a decision tree model. The classification standard of the selected prediction model is composed of the slope angle, the coefficient of permeability and the void ratio in the order. The computer program, SHAPP ver. 1.0 for prediction of slope hazards around an important national facilities using GIS technique and the developed model. To prove the developed prediction model and the computer program, the field data surveyed from Jumunjin, Gangneung city were compared with the prediction result in the same site. As the result of comparison, the real occurrence location of slope hazards was similar to the predicted section. Through the continuous study, the accuracy about prediction result of slope hazards will be upgraded and the computer program will be commonly used in practical.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.775-780
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• 2019

Techniques for predicting the future can be categorized into statistics-based and deep-run-based techniques. Among them, statistic-based techniques are widely used because simple and highly accurate. However, working-level officials have difficulty using many analytical techniques correctly. In this study, we compared the accuracy of prediction by applying multinomial logistic regression, decision tree, random forest, support vector machine, and Bayesian inference to marketing related data. The same marketing data was used, and analysis was conducted by using R. The prediction results of various techniques reflecting the data characteristics of the marketing field will be a good reference for practitioners.

• 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.

• 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 Intelligence and Information Systems
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• v.15 no.3
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• pp.151-165
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• 2009

Fuzzy neural network is an integrated model of artificial neural network and fuzzy system and it has been successfully applied in control and forecasting area. Recently ANFIS(Adaptive Network-based Fuzzy Inference System) has been noticed widely among various fuzzy neural network models because of its outstanding accuracy of control and forecasting area. We design a new classification model based on ANFIS and evaluate it in terms of classification accuracy. We identified ANFIS-based classification model has higher classification accuracy compared to existing classification model, C5.0 decision tree model by comparing their experimental results.

• Journal of Service Research and Studies
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• v.3 no.1
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• pp.9-16
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• 2013

Data analysis is the universal classification techniques, which requires a lot of effort. It can be easily analyzed to understand the results. Decision tree which is developed by Breiman can be the most representative methods. There are two core contents in decision tree. One of the core content is to divide dimensional space of the independent variables repeatedly, Another is pruning using the data for evaluation. In classification problem, the response variables are categorical variables. It should be repeatedly splitting the dimension of the variable space into a multidimensional rectangular non overlapping share. Where the continuous variables, binary, or a scale of sequences, etc. varies. In this paper, we obtain the coefficients of precision, reproducibility and accuracy of the classification tree to classify and evaluate the performance of the new cases, and through experiments to evaluate.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.639-652
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• 2015

In today's manufacturing environment, the process data can be easily measured and transferred to a computer for analysis in a real-time mode. As a result, it is possible to monitor several correlated quality variables simultaneously. Various multivariate statistical process control (MSPC) procedures have been presented to detect an out-of-control event. Although the classical MSPC procedures give the out-of-control signal, it is difficult to determine which variable has caused the signal. In order to solve this problem, data mining and machine learning techniques can be considered. In this paper, we applied the technique of decision tree learning to the MSPC, and we did simulation for MSPC procedures to monitor the bivariate normal process means. The results of simulation show that the overall performance of the MSPC procedure using decision tree learning technique is similar for several values of correlation coefficient, and the accurate classification rates for out-of-control are different depending on the values of correlation coefficient and the shift magnitude. The introduced procedure has the advantage that it provides the information about assignable causes, which can be required by practitioners.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.386-390
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• 2003

Data mining is a process of discovering useful patterns for decision making from an amount of data. It has recently received much attention in a wide range of business and engineering fields. Classifying a group into subgroups is one of the most important subjects in data mining. Tree-based methods, known as decision trees, provide an efficient way to finding the classification model. The primary concern in tree learning is to minimize a node impurity, which is evaluated using a target variable in the data set. However, there are situations where multiple target variable should be taken into account, for example, such as manufacturing process monitoring, marketing science, and clinical and health analysis. The purpose of this article is to present some methods for measuring the node impurity, which are applicable to data sets with multivariate target variables. For illustration, a numerical cxample is given with discussion.

• Proceedings of the Safety Management and Science Conference
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• 2005.11a
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• pp.324-327
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• 2005

급속도로 발전하는 산업의 고도화와 이에 따른 업종의 다양화, 이에 동반되는 예상치 못한 산업재해는 불특정 다수에게 인적, 물적 피해를 야기 시키고 있다. 산업재해 예방을 위해 다양한 선행 연구들이 진행되었으나 이들 연구는 기존의 산업재해 데이터를 토대로 빈도분석, 비교분석을 통한 관리적, 교육적 등치 대책만을 제시하고 있다. 본 연구에서는 산업재해 예방을 위해 객관적이고 정량화된 데이터를 통한 예측 분석이 가능한 데이터마이닝을 적용하여 대표적인 기법인 의사결정나무의 CHAID, CART, C4.5, QUEST 4가지 알고리즘 비교분석하여 산업재해 예방 및 전문가 시스템 구축을 위해 적용할 수 있는 최적의 알고리즘을 제시하도록 한다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1667-1676
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• 2006

다른 산업과 달리 병원/의료 산업에서는 건강 보험료 심사 평가라는 독특한 검증 과정이 필수적으로 있게 된다. 건강 보험료 심사 평가는 병원의 수익 문제 뿐 아니라 적정한 진료행위를 하는 병원이라는 이미지와도 맞물려 매우 중요한 분야이며, 특히 대형 종합병원일수록 이 부분에 많은 심사관련 인력들을 투입하여, 병원의 수익과 명예를 위해서 업무를 수행하고 있다. 본 논문은 이러한 건강보험료 청구 심사 과정에서, 사전에 수많은 진료 청구 건 중 심사 평가에서 삭감이 될 수 있는 진료 청구 건을 데이터 마이닝을 통해서 발견하여, 사전의 대비를 철저히 하고자 하는 한 국내의 대형 종합병원의 사례를 소개하고자 한다. 데이터 마이닝을 적용함에 있어, 주요한 문제점 중의 하나는 바로 지도학습 기법을 적용하기에 곤란한 데이터 불균형 문제가 발생하는 것이다. 이런 불균형 문제를 해소하고, 비교 조건 중에 가장 효율적인 삭감 예상 진료 건 탐지 모형을 만들어 내기 위하여 데이터 불균형 문제의 기본 해법인 과, Sampling 오분류 비용의 다양하고 혼합적인 적용을 통하여, 적합한 조건을 가지는 의사결정 나무 모형을 도출하였다.