• Title/Summary/Keyword: CART 모형

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Developing the Traffic Accident Prediction Model using Classification And Regression Tree Analysis (CART분석을 이용한 교통사고예측모형의 개발)

  • Lee, Jae-Myung;Kim, Tae-Ho;Lee, Yong-Taeck;Won, Jai-Mu
    • International Journal of Highway Engineering
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    • v.10 no.1
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    • pp.31-39
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    • 2008
  • Preventing the traffic accident by accurately predicting it in advance can greatly improve road traffic safety. The accurate traffic accident prediction model requires not only understanding of the factors that cause the accident but also having the transferability of the model. So, this paper suggest the traffic accident diagram using CART(Classification And Regression Tree) analysis, developed Model is compared with the existing accident prediction models in order to test the goodness of fit. The results of this study are summarized below. First, traffic accident prediction model using CART analysis is developed. Second, distance(D), pedestrian shoulder(m) and traffic volume among the geometrical factors are the most influential to the traffic accident. Third. CART analysis model show high predictability in comparative analysis between models. This study suggest the basic ideas to evaluate the investment priority for the road design and improvement projects of the traffic accident blackspots.

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Optimal Weather Variables for Estimation of Leaf Wetness Duration Using an Empirical Method (결로시간 예측을 위한 경험모형의 최적 기상변수)

  • K. S. Kim;S. E. Taylor;M. L. Gleason;K. J. Koehler
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.4 no.1
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    • pp.23-28
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    • 2002
  • Sets of weather variables for estimation of LWD were evaluated using CART(Classification And Regression Tree) models. Input variables were sets of hourly observations of air temperature at 0.3-m and 1.5-m height, relative humidity(RH), and wind speed that were obtained from May to September in 1997, 1998, and 1999 at 15 weather stations in iowa, Illinois, and Nebraska, USA. A model that included air temperature at 0.3-m height, RH, and wind speed showed the lowest misidentification rate for wetness. The model estimated presence or absence of wetness more accurately (85.5%) than the CART/SLD model (84.7%) proposed by Gleason et al. (1994). This slight improvement, however, was insufficient to justify the use of our model, which requires additional measurements, in preference to the CART/SLD model. This study demonstrated that the use of measurements of temperature, humidity, and wind from automated stations was sufficient to make LWD estimations of reasonable accuracy when the CART/SLD model was used. Therefore, implementation of crop disease-warning systems may be facilitated by application of the CART/SLD model that inputs readily obtainable weather observations.

Missing Value Imputation Method Using CART : For Marital Status in the Population and Housing Census (CART를 활용한 결측값 대체방법 : 인구주택총조사 혼인상태 항목을 중심으로)

  • 김영원;이주원
    • Survey Research
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    • v.4 no.2
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    • pp.1-21
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    • 2003
  • We proposed imputation strategies for marital status in the Population and Housing Census 2000 in Korea to illustrate the effective missing value imputation methods for social survey. The marital status which have relatively high non-response rates in the Census are considered to develope the effective missing value imputation procedures. The Classification and Regression Tree(CART)is employed to construct the imputation cells for hot-deck imputation, as well as to predict the missing value by model-based approach. We compare to imputation methods which include the CART model-based imputation and the sequential hot-deck imputation based on CART. Also we check whether different modeling for each region provides the more improved results. The results suggest that the proposed hot-deck imputation based on CART is very efficient and strongly recommendable. And the results show that different modeling for each region is not necessary.

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A Study on Exploration of the Recommended Model of Decision Tree to Predict a Hard-to-Measure Mesurement in Anthropometric Survey (인체측정조사에서 측정곤란부위 예측을 위한 의사결정나무 추천 모형 탐지에 관한 연구)

  • Choi, J.H.;Kim, S.K.
    • 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.

Development of a Soil Moisture Estimation Model Using Artificial Neural Networks and Classification and Regression Tree(CART) (의사결정나무 분류와 인공신경망을 이용한 토양수분 산정모형 개발)

  • Kim, Gwangseob;Park, Jung-A
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.31 no.2B
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    • pp.155-163
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    • 2011
  • In this study, a soil moisture estimation model was developed using a decision tree model, an artificial neural networks (ANN) model, remotely sensed data, and ground network data of daily precipitation, soil moisture and surface temperature. Soil moisture data of the Yongdam dam basin (5 sites) were used for model validation. Satellite remote sensing data and geographical data and meteorological data were used in the classification and regression tree (CART) model for data classification and the ANNs model was applied for clustered data to estimate soil moisture. Soil moisture data of Jucheon, Bugui, Sangjeon, Ahncheon sites were used for training and the correlation coefficient between soil moisture estimates and observations was between 0.92 to 0.96, root mean square error was between 1.00 to 1.88%, and mean absolute error was between 0.75 to 1.45%. Cheoncheon2 site was used for validation. Test statistics showed that the correlation coefficient, the root mean square error, the mean absolute error were 0.91, 3.19%, and 2.72% respectively. Results demonstrated that the developed soil moisture model using CART and ANN was able to apply for the estimation of soil moisture distribution.

Forecasting the Daily Container Volumes Using Data Mining with CART Approach (Datamining 기법을 활용한 단기 항만 물동량 예측)

  • Ha, Jun-Su;Lim, Chae Hwan;Cho, Kwang-Hee;Ha, Hun-Koo
    • Journal of Korea Port Economic Association
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    • v.37 no.3
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    • pp.1-17
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    • 2021
  • Forecasting the daily volume of container is important in many aspects of port operation. In this article, we utilized a machine-learning algorithm based on decision tree to predict future container throughput of Busan port. Accurate volume forecasting improves operational efficiency and service levels by reducing costs and shipowner latency. We showed that our method is capable of accurately and reliably predicting container throughput in short-term(days). Forecasting accuracy was improved by more than 22% over time series methods(ARIMA). We also demonstrated that the current method is assumption-free and not prone to human bias. We expect that such method could be useful in a broad range of fields.

Identification of major risk factors association with respiratory diseases by data mining (데이터마이닝 모형을 활용한 호흡기질환의 주요인 선별)

  • Lee, Jea-Young;Kim, Hyun-Ji
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.2
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    • pp.373-384
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    • 2014
  • Data mining is to clarify pattern or correlation of mass data of complicated structure and to predict the diverse outcomes. This technique is used in the fields of finance, telecommunication, circulation, medicine and so on. In this paper, we selected risk factors of respiratory diseases in the field of medicine. The data we used was divided into respiratory diseases group and health group from the Gyeongsangbuk-do database of Community Health Survey conducted in 2012. In order to select major risk factors, we applied data mining techniques such as neural network, logistic regression, Bayesian network, C5.0 and CART. We divided total data into training and testing data, and applied model which was designed by training data to testing data. By the comparison of prediction accuracy, CART was identified as best model. Depression, smoking and stress were proved as the major risk factors of respiratory disease.

일상어휘를 기반으로 한 선물 가격 예측모형의 계발

  • 김광용;이승용
    • Proceedings of the Korea Database Society Conference
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    • 1999.06a
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    • pp.291-300
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    • 1999
  • 본 논문은 인공신경망과 귀납적 학습방법 등의 인공지능 방법과 선물가격결정에 대한 기존 재무이론을 사용하여 일상어취로 표현되는 파생상품 가격예측 모형을 개발하는데 있다. 모형의 개발은 1단계로 인공신경망이나 기존의 선물가격결정이론(평균보 유비용모형이나 일반균형모형)을 이용하여 선물 가격을 예측한 후, 서로 비교 분석하여 인공신경망 모형의 우수성을 확인하였다. 귀납적 학습방법중 CART 알고리듬을 사용하여 If-Then 규칙을 생성하였다. 특히 실용적 측면에서 선물가격의 일상어휘화를 통한 모형개발을 여러 가지 방법으로 시도하였다. 이러한 선물가격 예측모형의 유용성은 일단 If-Then 규칙으로 표현되어 전문가의 판단에 확실한 이론적인 근거를 제시할 수 있는 장점이 있으며, 특히 의사결정지원시스템으로 활용화 될 경우 매우 유용한 근거자료로 활용될 수 있다. 이러한 선물가격 예측모형의 정확성은 분석표본과 검증표본으로 나누어 검증표본에서 세가지 기본모형(평균보유 비용모형, 일반균형모형, 인공신경망 모형)과 각 모형의 귀납적 학습방법 모형의 다른 3가지 어휘표현방법 3가지를 모형별로 비교 분석하였다. 분석결과 인공신경망모형은 상당한 예측력을 갖고 있는 것으로 판명되었으며, 특히 CART를 기반으로 한 일상어취 기반의 선물가격예측 모형은 예측력이 높은 것으로 나타났다.

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The Comparison of OC1 and CART for Prosodic Boundary Index Prediction (운율 경계강도 예측을 위한 OC1의 적용 및 CART와의 비교)

  • 임동식;김진영;김선미
    • The Journal of the Acoustical Society of Korea
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    • v.18 no.4
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    • pp.60-64
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    • 1999
  • In this paper, we apply CART(Classification And Regression tree) and OC1(Oblique Classifier1) which methods are widely used for continuous speech recognition and synthesis. We prediet prosodic boundary index by applying CART and OC1, which combine right depth of tree-structured method and To_Right of link grammar method with tri_gram model. We assigned four prosodic boundary index level from 0 to 3. Experimental results show that OC1 method is superior to CART method. In other words, in spite of OC1's having fewer nodes than CART, it can make more improved prediction than CART.

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Development of Selection Model of Subway Station Influence Area (SIA) in New town using Categorical and Regression Tree (CART) (CART분석을 이용한 신도시지역의 지하철 역세권 설정에 관한 연구)

  • Kim, Tae-Ho;Lee, Yong- Taeck;Hwang, E-Pyo;Won, Jai-Mu
    • Journal of the Korean Society for Railway
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    • v.11 no.3
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    • pp.216-224
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    • 2008
  • In general, based on criteria of subway law, radius 500m from subway station is defined as SIA(Subway Station Influence Area). Therefore, in this paper, selection models of SIA are developed to identify appropriate SIA for recently developed 4 new towns based based on CART analysis. As a result, following outputs are obtained; (1) walking distance from subway station is the most influential factor to define SIA (2) SIAs vary with new towns (i.e., bundang city: 856m, ilsan sanbon city 508m, pyungchon city 495m), and (3) walking distance from subway station is influential to land price of SIA. In addition, bundang and pyungchon new town are more affected in land price and walking distance. Therefore, it is desirable for current definition of SIA (radius 500m from subway station) to reflect characteristics of land use and walking distance in the new towns.