• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.257-260
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• 2000

본 연구에서는 의사결정나무모형을 이용하여 교통사고 유형 분석을 시도한다. 분석에 이용된 자료는 도로교통안전관리공단에서 수집한 교통사고 정밀조사 자료이다. 본 연구에서 목표변수는 '사고내용'이며, 설명변수는 '인적 요인', '차량적 요인', '도로 환경적 요인' 관련 변수이다. 목표변수에 주요한 기여를 하는 주요 설명변수를 도출하였으며, 얻어진 의사결정나무모형을 토대로 하여 교통사고를 유형화하였다.

• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.923-935
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• 2009

This study aims to explore a recommended model of decision tree to predict a hard-to-measure measurement in anthropometric survey. We carry out an experiment on cross validation study to obtain a recommened model of decision tree. We use three split rules of decision tree, those are CHAID, Exhaustive CHAID, and CART. CART result is the best one in real world data.

• Journal of Korean Society of Transportation
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• v.28 no.6
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• pp.43-54
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• 2010

In recent years there have been studies on tour based approaches for freight travel demand modelling. The purpose of this paper is to analyze tour type choice behavior of commercial vehicles which are divided into round trips and chained tours. The methods of the study are based on the decision tree and the logit model. The results indicates that the explanation variables for classifying tour types of commercial vehicles are loading factor, average goods quantity, and total goods quantity. The results of the decision tree method are similar to those of logit model. In addition, the explanation variables for tour type classification of small trucks are not different from those for medium trucks', implying that the most important factor on the vehicle tour planning is how to load goods such as shipment size and total quantity.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.671-678
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• 2011

Data mining searches for interesting relationships among items in a given database. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, customer classification, etc. When create decision tree model, complicated model by standard of model creation and number of input variable is produced. Specially, there is difficulty in model creation and analysis in case of there are a lot of numbers of input variable. In this study, we study on decision tree using intervening variable. We apply to actuality data to suggest method that remove unnecessary input variable for created model and search the efficiency.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.217-229
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• 2018

This paper aims at applying the data mining such as decision tree, neural network, and logistic regression to an unsold apartment complex in Wirye new town and developing the model forecasting the result of initial sale contract by house unit. Raw data are divided into training data and test data. The order of predictability in training data is neural network, decision tree, and logistic regression. On the contrary, the results of test data show that logistic regression is the best model. This means that logistic regression has more data adaptability than neural network which is developed as the model optimized for training data. Determinants of initial sale are the location of floor, direction, the location of unit, the proximity of electricity and generator room, subscriber's residential region and the type of subscription. This suggests that using two models together is more effective in exploring determinants of initial sales. This paper contributes to the development of convergence field by expanding the scope of data mining.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.877-884
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• 2011

The decision tree is a representative algorithm of data mining and used in many domains such as retail target marketing, fraud detection, data reduction, variable screening, category merging, etc. This method is most useful in classification problems, and to make predictions for a target group after dividing it into several small groups. When we create a model of decision tree with a large number of input variables, we suffer difficulties in exploration and analysis of the model because of complex trees. And we can often find some association exist between input variables by external variables despite of no intrinsic association. In this paper, we study on the removal method of unnecessary input variables using multiple external association rules. And then we apply the removal method to actual data for its efficiencies.

• 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 Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.145-159
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• 2011

National long-term care insurance started in July, 2008. We try to make up for weak points and develop a long-term care insurance system. Especially, it is important to upgrade the rating model of the category of need for long-term care continually. We improve the rating model using the data after enforcement of the system to reflect the rapidly changing long-term care marketplace. A decision tree model was adpoted to upgrade the rating model that makes it easy to compare with the current system. This model is based on the first assumption that, a person with worse functional conditions needs more long-term care services than others. Second, the volume of long-term care services are de ned as a service time. This study was conducted to reflect the changing circumstances. Rating models have to be continually improved to reflect changing circumstances, like the infrastructure of the system or the characteristics of the insurance beneficiary.

• International Journal of Highway Engineering
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• v.13 no.3
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• pp.167-176
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• 2011

This study's purpose is to analyse factors of determination about detouring for makinga standard model in regard of unfavorableness and uncertainty when unspecified individual recipients make a decision at the time of course detour. In order to achieve this, we surveyed SP investigation whether making a detour or not for drivers as a target who take a high way and National highway. Based on this result, we analysed detour determination factors of drivers, establishing a combination model of Decision Tree and Neural Network model. The result demonstrates the effected factors on drivers' detour determination are in ordering of the recognition of alternative routevs, reliable and frequency of using traffic information, frequency of transition routes and age. Moreover, from the outcome in comparison with an existing model and prediction through undistributed data, the rate of combination model 8.7% illustrates the most predictable way in contrast with logit model 12.8%, and Individual Model of Decision Tree 13.8% which are existed. This reveals that the analysis of drivers' detour determination factors is valid to apply. Hence, overall study considers as a practical foundation to make effective detour strategies for increasing the utility of route networking and dispersion in the volume of traffic from now on.