• Journal of Korean Institute of Industrial Engineers
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• v.26 no.2
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• pp.171-182
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• 2000

For the last five years, data mining has drawn much attention by researchers and practitioners because of its many applicable domains. This article presents an adaptive decision tree algorithm for dynamically reasoning machine failure cause out of real-time, large-scale machine status database. Among many data mining methods, intelligent decision tree building algorithm is especially of interest in the sense that it enables the automatic generation of decision rules from the tree, facilitating the construction of expert system. On the basis of experiment using semiconductor etching machine, it has been verified that our model outperforms previously proposed decision tree models.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.959-969
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• 2009

Decision tree is a typical tool for data classification. This tool is implemented in DAVIS (Huh and Song, 2002). All the visualization tools and statistical clustering tools implemented in DAVIS can communicate with the decision tree. This paper presents methods to apply data visualization techniques to the decision tree using a real data set.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.507-509
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• 2012

심전도 질환 데이터는 일반적으로 분류기를 사용한 실험이 많다. 심전도 신호는 QRS-Complex와 R-R interval을 추출하는 경우가 많은데 본 실험에서는 R-R interval을 추출하여 실험하였다. 심전도 데이터의 분류 실험은 일반적으로 SVM(Support Vector Machine)과 MLP(Multilayer Perceptron)으로 실험되지만 본 실험은 Decision Tree를 사용하여 정확도 향상을 추구하였다. 그리고 정확도 비교 분석을 위해 SVM과 MLP 분류기 실험을 같이 수행하였고, 동일한 데이터와 간격으로 실험한 타 논문의 결과와 비교해 보았다. Decision Tree를 다른 분류기와 타 논문의 결과와 비교해 보니 정확도 부분에서는 Decision Tree가 가장 우수하였다.

• Journal of Sasang Constitutional Medicine
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• v.13 no.2
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• pp.144-155
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• 2001

By the increasing concern about Sasang Constitution Medicine, its practical use is considered very important in disease prevention and medical treatment. However, the method of constitution classification is depending on the doctor's clinical trials because of the lack of the objective test criteria. This study is trying to improve the objectiveness of diagnosis using a new statistical method, decision tree. Decision tree method-a classification technique in the statistical analysis- was used to analyze the result of QSCCII instead of using discriminant analysis. As a result, 16 among 121 QSCCII questions was selected as important questions and 21 terminal nodes was built to classify the constitution. Using only 16 questions shown in the result of decision tree, we can diagnose and interpret the constitution easily and effectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.2
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• pp.92-99
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• 2014

This paper proposes the method of facial expression recognition based on decision tree structure. In the image of facial expression, ASM(Active Shape Model) and LBP(Local Binary Pattern) make the local features of a facial expressions extracted. The discriminant features gotten from local features make the two facial expressions of all combination classified. Through the sum of true related to classification, the combination of facial expression and local region are decided. The integration of branch classifications generates decision tree. The facial expression recognition based on decision tree shows better recognition performance than the method which doesn't use that.

• International Journal of Highway Engineering
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• v.16 no.4
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• pp.127-134
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• 2014

PURPOSES : To operate more efficient traffic management system, it is utmost important to detect the change in congestion level on a freeway segment rapidly and reliably. This study aims to develop classification method of congestion change type. METHODS: This research proposes two classification methods to capture the change of the congestion level on freeway segments using the dedicated short range communication (DSRC) data and the vehicle detection system (VDS) data. For developing the classification methods, the decision tree models were employed in which the independent variable is the change in congestion level and the covariates are the DSRC and VDS data collected from the freeway segments in Korea. RESULTS : The comparison results show that the decision tree model with DSRC data are better than the decision tree model with VDS data. Specifically, the decision tree model using DSRC data with better fits show approximately 95% accuracies. CONCLUSIONS : It is expected that the congestion change type classified using the decision tree models could play an important role in future freeway traffic management strategy.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.2
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• pp.19-29
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• 2015

The purpose of this study is to suggest the grid search method for selecting an optimal decision tree model. It chooses optimal values for the maximum depth of tree and the minimum number of observations that must exist in a node in order for a split to be attempted. Therefore, the grid search method guarantees building a decision tree model that shows more precise and stable classifying performance. Through empirical analysis using data of job satisfaction of workplace reserve force commanders, we show that the grid search method helps us generate an optimal decision tree model that gives us hints for the improvement direction of labor conditions of Korean workplace reserve force commanders.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.299-307
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• 2012

Data mining is a method of searching for an interesting relationship among items in a given database. The decision tree is a typical algorithm of data mining. The decision tree is the method that classifies or predicts a group as some subgroups. In general, when researchers create a decision tree model, the generated model can be complicated by the standard of model creation and the number of input variables. In particular, if the decision trees have a large number of input variables in a model, the generated models can be complex and difficult to analyze model. When creating the decision tree model, if there are marginally conditional variables (intervening variables, external variables) in the input variables, it is not directly relevant. In this study, we suggest the method of creating a decision tree using marginally conditional variables and apply to actual data to search for efficiency.

• Proceedings of the KSR Conference
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• 2006.05b
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• pp.1179-1187
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• 2006

• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.223-225
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• 2002

Decision tree는 목표 데이터에 대한 계층적 관점을 보여준다는 의미에서 데이터를 보다 잘 이해하는데 많은 도움이 되나 탐욕법(greedy algorithm)에 의한 트리 생성법의 한계로 인해 최적의 예측자라고는 할 수가 없다. 이와 같은 약점을 보완하기 위하여 일반적 방법으로 생성한 decision tree에 대하여 다차원 연관규칙 알고리즘을 적용함으로써 짱은 길이의 최적 부분 규칙집합을 구하는 방법을 제시하였고 실험을 통해 그와 같은 사실을 확인하였다.