• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.179-190
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• 2009

In the financial industry, the decision tree algorithm has been widely used for classification analysis. In this case one of the major difficulties is that there are so many explanatory variables to be considered for modeling. So we do need to find effective method for reducing the number of explanatory variables under condition that the modeling results are not affected seriously. In this research, we try to compare the various variable reducing methods and to find the best method based on the modeling accuracy for the tree algorithm. We applied the methods on the pension insurance of a insurance company for getting empirical results. As a result, we found that selecting variables by using the sensitivity analysis of neural network method is the most effective method for reducing the number of variables while keeping the accuracy.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.56-62
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• 2008

This paper proposes the use of decision tree classification for the measuring pattern recognition from industrial Computed Radiography(CR) images used in nondestructive evaluation(NDE) of steel-tubes. It appears that NDE problems are naturally desired to have machine learning techniques identify patterns and their classification. The attributes of decision tree are taken from NDE test procedure. Geometric features, such as radiative angle, gradient and distance, are estimated from the analysis of input image data. These factors are used to make it easy and accurate to classify an input object to one of the pre-specified classes on decision tree. This algerian is to simplify the characterization of NDE results and to facilitate the determination of features. The experimental results verify the usefulness of proposed algorithm.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.271-282
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• 2000

본 연구의 목적은 데이터마이닝 기법을 임상적으로 중요한 위치를 차지하고 있는 고혈압 환자의 특성과 치료에 따른 예후를 예측할 수 있는 지식을 발굴하고 이의 임상적용의 타당성을 검증하여 의사결정지원시스템을 개발하고 이의 유용성을 평가하는데 있다. 이에 연세대학교 의과대학 부속 세브란스 병원의 환자를 대상으로 로지스틱 회귀분석을 이용하여 혈압조절상의 위험요인의 규명하고, 의사결정나무분석을 통해 치료약제별 혈압조절군과 비조절군의 특성을 도출하고 각 대상군을 결정짓는 규칙을 생성하였으며, 이를 활용한 의사결정지원시스템의 개발 및c 평가를 시행하였다. 그 결과 기존 임상이론만을 활용한 시스템의 처방에 의한 혈압조절군보다 데이터마이닝 기법을 활용한 시스템의 처방에 의한 혈압조절군의 비율이 전체적으로 더 높게 나타남을 알 수 있었다. 본 연구의 결과는 우리나라 현실에 부합되는 고혈압 진료지침을 개발하고 적용, 평가하는데 기여할 수 있을 것으로 판단되며, 이와 같은 의사결정지원 시스템을 운영을 통해 실제 임상 진료에 적용해 봄으로써 그 효과와 실증적 가치를 창출할 수 있을 것이다.

• Journal of Distribution Science
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• v.8 no.1
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• pp.13-19
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• 2010

Segmentation, targeting, and positioning are marketing tools used by a company to gain competitive advantage in the market. For an accurate segmentation, various statistics models or datamining techniques are used. Especially, datamining techniques are introduced in the beginning of the 1980s and solved several marketing problems effectively. In this paper, we research about datamining technique for segmentation and analyze customer's transaction data of Department Store using Decision Tree Analysis, one of the dataming technique. After that, we discuss effects and advantages of segmentation using Decision Tree.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.280-286
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• 2005

본 논문은 이분형 목적변수를 가지는 데이터에서, 의사결정나무나 신경망과 같은 지도 학습(Supervised Learning)의 훈련을 통한 각종 예측 및 분류 정확도를 향상시키기 위해서 오차 패턴을 이용한 새로운 Hybrid 데이터 마이닝 기법을 제안한다. 오차 패턴을 이용한 Hybrid 기법이란 데이터 마이닝의 서로 다른 기법을 각 데이터에 적용한 다음 기법간의 불일치되는 부분만을 다시 패턴화 하여, 이를 최종 모형에 적용하여, 기존에 1개의 방법만을 사용하였을 경우보다, 더욱 좋은 정확도를 가질 수 있도록 하는 방법이다. 본 기법의 검증을 위하여, 10개의 실제 검증용 자료를 사용하였으며, 분석 결과 신경망과 의사결정나무 분석과 같은 기존의 방법보다 전체적으로 예측력이 향상됨을 보였다.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.593-601
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• 2017

In this paper, we constructed the model of production/investment, distribution, and screening by using variables that can be considered at each stage according to the value chain stage of the movie industry. To increase the predictive power of the model, a regression analysis was used to derive meaningful variables. Based on the given variables, we compared the difference in predictive power between the artificial neural network, which is a machine learning analysis method, and the decision tree analysis method. As a result, the accuracy of artificial neural network was higher than that of decision trees when all variables were added in production/ investment model and distribution model. However, decision trees were more accurate when selected variables were applied according to regression analysis results. In the screening model, the accuracy of the artificial neural network was higher than the accuracy of the decision tree regardless of whether the regression analysis result was reflected or not. This paper has an implication which we tried to improve the performance of movie prediction model by using machine learning analysis. In addition, we tried to overcome a limitation of linear approach by reflecting the results of regression analysis to ANN and decision tree model.

• The Journal of Information Systems
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• v.20 no.3
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• pp.237-255
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• 2011

지난 수십 년 간 연구자들은 효과적인 진료지원시스템을 개발하기 위해 다양한 도구와 방법론들을 제안하였고 지금도 새로운 방법론과 도구들을 계속적으로 개발하고 있다. 그 중에서 흉통으로 응급실에 내원한 노인환자에 대한 정확한 진단은 중요한 이슈 중의 하나였다. 따라서 많은 연구자들이 의사의 진단 능력을 향상시키기 위한 지능적인 의료의사결정과 시스템 개발에 투신하고 있지만 전통적인 의료시스템에 따른 대부분의 진료의사결정이 단일 분류기(classifier)에 기반하고 있어 만족스런 성능을 보여주지 못하고 있는 것이 현실이다. 따라서 이 논문은 앙상블 전략을 활용하여 의사들이 노인환자들의 흉통을 더 정확하고 빠르게 진단하는데 있어 도움을 줄 수 있게 하였다. 의사결정나무, 인공신경망, SVM 모델을 결합한 앙상블 기법을 실제 응급실에서 수집한 응급실 자료에 적용하였고, 그 결과 단일 분류기를 사용하는 것에 비해 월등히 향상된 진단 성과를 보이는 것을 관찰 할 수 있었다.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.777-786
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• 2018

This study propose a new method to detect Cochlodinium polykrikoides on satellite images using logistic regression and decision tree. We used spectral profiles(918) extracted from red tide, clear water and turbid water as training data. The 70% of the entire data set was extracted and used for model training, and the classification accuracy of the model was evaluated by using the remaining 30%. As a result of the accuracy evaluation, the logistic regression model showed about 97% classification accuracy, and the decision tree model showed about 86% classification accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.661-674
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• 2021

Edwardsiellosis is difficult to treat in cultured olive flounder, Paralichthys olivaceus. It is present in the fish for a long period during all growth stages, and it often leads to mass mortalites. In this paper, the clinical patterns of Edwardsiellosis were analyzed by dividing the data into the whole-water temperature, low-water temperature, low-high water temperature, high-water temperature, and high-low water temperature groups based on various clinical signs of diseased cultured olive flounder using a decision tree technique. In the clinical sign patterns in the decision trees analyzed in the experiment, clinical signs in the liver, such as liver nodules, liver hemorrhages, and liver degeneration, were selected as the criteria for determining Edwardsiellosis. The selected clinical signs were known as the major clinical signs of Edwardsiellosis, and through consultation with fishery disease experts, the analysis confirmed that the clinical signs of Edwardsiellosis were successfully found in this study.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.517-525
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• 2022

In the era of big data, various data mining techniques have been proposed as major analysis methodologies. As complex and diverse data is mass-produced, data mining techniques have attracted attention as a method that forms the foundation of data science. In this paper, we focused on the decision tree, which is frequently used in practice and easy to understand as one of representative data mining methods. Specifically, we analyzed the effect of the splitting method of decision trees on the model performance. We compared the prediction power and structures of decision tree models with different split methods based on various simulated data. The results show that the linear combination split method can improve the prediction accuracy of decision trees in the case of data simulated from nonlinear models with complex structure.