• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.94-100
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• 2010

Data Mining is mainly used for pattern extracting and information discovery from collected data. However previous methods is difficult to reflect changing patterns with time. In this paper, we introduce Streaming Decision Tree(SDT) analyzing data with continuity, large scale, and changed patterns. SDT defines continuity data as blocks and extracts rules using a Decision Tree's learning method. The extracted rules are combined considering time of occurrence, frequency, and contradiction. In experiment, we applied time series data and confirmed resonable result.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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 Korean Society for Quality Management Conference
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• 2006.11a
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• pp.119-123
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• 2006

본 연구는 말콤볼드리지(ME) 모델에 근거하여 경영진의 의사결정을 분석하는 것이다. 경영진의 회의록 분석을 통해서 경영방향을 분석하는 방법과 결과 활용을 논한다. 데이터마이닝의 기법인 의사결정나무를 이용하여 의사결정의 패턴을 찾는 방법도 소개한다.

• 한국경영정보학회:학술대회논문집
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• 2007.11a
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• pp.251-256
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• 2007

성형외과에서는 웹 방문자를 늘리기 위하여 다양한 노력을 하고 있지만, 웹 사이트의 어떠한 속성이 웹 방문자 수를 증대시키는지에 대한 체계적인 연구는 찾아보기 어렵다. 본 논문에서는 방문자 수가 많은 성형외과 웹 사이트와 방문자 수가 적은 웹 사이트를 구분하는 속성을 규명하였다. 다중 판별 분석과 의사결정 나무 기법, 신경망 분석 기법을 이용하여 방문자의 다소 (多少)를 구분하는 속성들을 도출하였다. 웹 사이트의 속성 중 '가상성형프로그램', '정보추천' 등 소수의 속성이 방문자 수의 다소(多少)를 설명하는 것으로 드러났다.