• Management Science and Financial Engineering
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• v.19 no.1
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• pp.1-10
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• 2013

Hierarchical forecasting strategy does not always outperform direct forecasting strategy. The performance generally depends on demand features. This research guides the use of the alternative forecasting strategies according to demand features. This paper developed and evaluated various classification models such as logistic regression (LR), artificial neural networks (ANN), decision trees (DT), boosted trees (BT), and random forests (RF) for predicting the relative performance of the alternative forecasting strategies for the South Korean navy's spare parts demand which has non-normal characteristics. ANN minimized classification errors and inventory costs, whereas LR minimized the Brier scores and the sum of forecasting errors.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.459-473
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• 2004

It has well known that an exhaustive search algorithm suggested by Breiman et. a1.(1984) has a trend to select the variable having relatively many possible splits as an splitting rule. We propose an algorithm to overcome this variable selection bias problem and then construct unbiased regression trees based on the algorithm. The proposed algorithm runs two steps of selecting a split variable and determining a split rule for binary split based on the split variable. Simulation studies were performed to compare the proposed algorithm with Breiman et a1.(1984)'s CART(Classification and Regression Tree) in terms of degree of variable selection bias, variable selection power, and MSE(Mean Squared Error). Also, we illustrate the proposed algorithm with real data sets.

• Journal of Korean Society for Quality Management
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• v.36 no.1
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• pp.72-79
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• 2008

The policy for credit card approval/disapproval is based on the applier's personal and financial information. In this paper, we will analyze 2 credit card approval data with several classification methods. We identify which variables are important factors to decide the approval of credit card. Our main tool is an open-source statistical programming environment R which is freely available from http://www.r-project.org. It is getting popular recently because of its flexibility and a lot of packages (libraries) made by R-users in the world. We will use most widely used methods, LDNQDA, Logistic Regression, CART (Classification and Regression Trees), neural network, and SVM (Support Vector Machines) for comparisons.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.08a
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• pp.155-159
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• 2008

In this thesis, in order to develop a new classification model of Sasang Constitutional medical types, which is helpful for improving the accuracy of diagnosis of medical types. various data-mining classification models such as discriminant analysis. decision trees analysis, neural networks analysis, logistics regression analysis, clustering analysis which are main classification methods were applied to the questionnaires of medical type classification. In this manner, a model which scientifically classifies constitutional medical types in the field of Sasang Constitutional Medicine, one of a traditional Korean medicine, has been developed. Also, the above-mentioned analysis models were systematically compared and analyzed. In this study, a classification of Sasang constitutional medical types was developed based on the discriminate analysis model and decision trees analysis model of which accuracy is relatively high, of which analysis procedure is easy to understand and to explain and which are easy to implement. Also, a diagnosis system of Sasang constitution was implemented applying the two analysis models.

• Computers and Concrete
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• v.16 no.5
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.526-533
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• 2011

Contracting is said to be a high-risk business, and a common cause of business failure is related to cash management. A contractor's financial viability depends heavily on how actual payments from an owner deviate from those defined in the contract. The paper presents a method for contractors to evaluate the punctuality and fullness of owner payments based on historical behaviour. It does this by classifying owners according to their late and incomplete payment practices. A payment profile of an owner, in the form of aging claims submitted by the contractor, is used as a basis for the method's development. Regression trees are constructed based on three predictor variables, namely, the average time to payment following a claim, the total amount ending up being paid within a certain period and the level of variability in claim response times. The Tree package in the publicly available R program is used for building the trees. The analysis is particularly useful for contractors at the pre-tendering stage, when contractors predict the likely payment scenario in an upcoming project. Based on the method, the contractor can decide whether to tender or not tender, or adjust its financial preparations accordingly. The paper is a contribution in risk management applied to claim and dispute resolution practice. It is argued that by contractors having a better understanding of owner payment behaviour, fewer disputes and contractor business failures will occur.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.886-890
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• 2007

Machine fault prognosis techniques have been considered profoundly in the recent time due to their profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are forecasted precisely before they reach the failure thresholds. In this work, we propose an approach of Least Square Regression Tree (LSRT), which is an extension of the Classification and Regression Tree (CART), in association with one-step-ahead prediction of time-series forecasting technique to predict the future conditions of machines. In this technique, the number of available observations is firstly determined by using Cao's method and LSRT is employed as prognosis system in the next step. The proposed approach is evaluated by real data of low methane compressor. Furthermore, the comparison between the predicted results of CART and LSRT are carried out to prove the accuracy. The predicted results show that LSRT offers a potential for machine condition prognosis.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.375-383
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• 2012

The purpose of the ensemble methods is to increase the accuracy of prediction through combining many classifiers. According to recent studies, it is proved that random forests and forward stagewise regression have good accuracies in classification problems. However they have great prediction error in separation boundary points because they used decision tree as a base learner. In this study, we use the kernel ridge regression instead of the decision trees in random forests and boosting. The usefulness of our proposed ensemble methods was shown by the simulation results of the prostate cancer and the Boston housing data.

• Journal of Digital Convergence
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• v.13 no.12
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• pp.187-192
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• 2015

If the software is developed to analyze the speech disorder, the application of various converged areas will be very high. This paper implements the user-friendly program based on CART(Classification and regression trees) analysis to distinguish between normal and pathological voices utilizing combination of the acoustical and HOS(Higher-order statistics) parameters. It means convergence between medical information and signal processing. Then the acoustical parameters are Jitter(%) and Shimmer(%). The proposed HOS parameters are means and variances of skewness(MOS and VOS) and kurtosis(MOK and VOK). Database consist of 53 normal and 173 pathological voices distributed by Kay Elemetrics. When the acoustical and proposed parameters together are used to generate the decision tree, the average accuracy is 83.11%. Finally, we developed a program with more user-friendly interface and frameworks.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.243-248
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• 2018

The social activities of the elderly are important in successfully achieving aging by providing opportunities for social interaction to enhance life satisfaction. The purpose of this study is to identify the related factors of the elderly social activities and build a statistical classification model to predict social activities. Subjects were 1,864 elderly people (829 males, 1,035 females) who completed the community health survey in 2015. Outcome variables were defined as the experience of social activity during the past month(yes, no). The prediction model was constructed using decision tree model based on Classification and Regression Trees (CRT) algorithm. The results of this study were subjective health, frequency of meeting with neighbors, frequency of meeting with relatives, and living with spouse were significant variables of social participation. The most prevalent predictor was the subjective health level. In order to prepare for the successful aging of the super aged society based on the results of this study, social attention and support for the social activities of the elderly are required.