• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.2
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• pp.1-11
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• 2014

In this paper, travel speed patterns were deducted based on historical DSRC travel speed data using Decision Tree technique to improve availability of the massive amount of historical data. These patterns were designed to reflect spatio-temporal vicissitudes in reality by generating pattern units classified by months, time of day, and highway sections. The study area was from Seoul TG to Ansung IC sections on Gyung-bu highway where high peak time of day frequently occurs in South Korea. Decision Tree technique was applied to categorize travel speed according to day of week. As a result, five different pattern groups were generated: (Mon)(Tue Wed Thu)(Fri)(Sat)(Sun). Statistical verification was conducted to prove the validity of patterns on nine different highway sections, and the accuracy of fitting was found to be 93%. To reduce travel pattern errors against individual travel speed data, inclusion of four additional variables were also tested. Among those variables, 'traffic condition on previous month' variable improved the pattern grouping accuracy by reducing 50% of speed variance in the decision tree model developed.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.297-309
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• 2020

This study focuses on the application of the data mining technique logistic regression analysis and decision tree analysis to the domestic public database called Korean Children Youth Panel Survey (KCYPS) to derive a series of important factors affecting the enhancement of life satisfaction of domestic youth. As a result, the general impact factors on life satisfaction for each grade were derived from logistic regression. Using decision tree analysis, we came to conclusions that those factors such as depression, overall grade satisfaction, household economic level, and school adaptation play crucial roles in affecting high school adolesscents' life satisfaction.

• 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 Institute of Intelligent Systems
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• v.15 no.6
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• pp.682-687
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• 2005

Behavioral sequences of the medaka(Oryzias latipes) were investigated through an image system in response to medaka treated with the insecticide and medaka not treated with the insecticide, diazinon(0.1 mg/1). After much observation, behavioral patterns could be divided into 4 patterns: active smooth, active shaking, inactive smooth, and inactive shaking. These patterns were analyzed by 5 features: speed ratio, x and y axes projection, FFT to angle transition, fractal dimension, and center of mass. Each pattern was classified using decision tree. It provide a natural way to incorporate prior knowledge from human experts in fish behavior, The main focus of this study was to determine whether the decision tree could be useful in interpreting and classifying behavior patterns of the animal.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.45-53
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• 2003

One of decision tree method is regression trees which are used to predict a continuous response. The general splitting criteria in tree growing are based on a compromise in the impurity between the left and the right child node. By picking or the more interesting subsets and ignoring the other, the proposed new splitting criteria in this paper do not split based on a compromise of child nodes anymore. The tree structure by the new criteria might be unbalanced but plausible. It can find a interesting subset as early as possible and express it by a simple clause. As a result, it is very interpretable by sacrificing a little bit of accuracy.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.226-229
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• 2010

본 연구에서는 데이터 저장의 질적 향상을 도모하는 XML 스키마 매칭의 효율적 활용방안을 제시하였다. 이를 위하여 매칭의 가중치의 변화에 따라 달라지는 정확도 데이터를 수집하고, 수집한 데이터를 활용하여 데이터 마이닝 기법 중 하나인 의사결정나무 모델을 수립하였다. 수립모델을 응용하여 구현한 가중치 자동선정 모듈은 설명변수인 교량의 형식, 문서가 포함하고 있는 요소의 수, 문서를 작성한 회사 등의 값에 따라 의사결정나무 모델의 목표변수인 정확도뿐만 아니라, 가장 높은 정확도를 보일 수 있는 가중치까지 간접적으로 제안가능하다. 본 연구로 구현한 모듈을 통해 제안된 XML 스키마 매칭 가중치를 활용하면 그렇지 않은 경우에 비하여 약 10% 정확도 상승효과가 있음을 알 수 있었다.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11c
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• pp.1351-1354
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• 2003

데이터 마이닝 기법 중 회귀분석 기법과 의사절정나무 분석 기법을 이용하여 국지기상예보칙을 작성하는 방안을 연구하였다. 회귀분석기법을 이용하여 예보값에 영향을 미치는 예보요소를 도출하고, 도출된 예보요소를 회귀분석 기법과 의사결정나무 분석 기법에 적용하여 예보칙을 작성하였다.

• Information Systems Review
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• v.9 no.1
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• pp.67-84
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• 2007

In diffusing an information systems(IS), the provider of the IS can be more effective if they can identify user groups who can adopt the system early. By focusing on the user groups, system providers can encourage them to adopt the IS. After the early adopters adopt an IS, the diffusion of the system to other groups can be easier by early adopters' voluntary advertisement and help in adopting the IS. Instead of discrete choice methods which are usually used for this purpose, we suggest a decision tree method. Compared to discrete choice methods, this method is more accurate for prediction and can easily identify non-linear segments of groups. By testing the data of adopters of an IS in agricultural business, we show the excellence of this method in identifying target groups to focus on. This method would help system providers to diffuse their systems by starting from early adopters.

• The Journal of the Korea Contents Association
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• v.7 no.8
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• pp.13-20
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• 2007

Decision tree is one of the most useful analysis methods for various data mining functions, including prediction, classification, etc, from massive data. Decision tree grows by splitting nodes, during which the purity increases. It is needed to stop splitting nodes when the purity does not increase effectively or new leaves does not contain meaningful number of records. Pruning is done if a branch does not show certain level of performance. By pruning, the structure of decision tree is changed and it is implied that the previous splitting of the parent node was not effective. It is also implied that the splitting of the ancestor nodes were not effective and the choices of attributes and criteria in splitting them were not successful. It should be noticed that new attributes or criteria might be selected to split such nodes for better tries. In this paper, we suggest a procedure to modify decision tree by Fuzzy theory and splitting as an integrated approach.