• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.2
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• pp.19-29
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• 2015

The purpose of this study is to suggest the grid search method for selecting an optimal decision tree model. It chooses optimal values for the maximum depth of tree and the minimum number of observations that must exist in a node in order for a split to be attempted. Therefore, the grid search method guarantees building a decision tree model that shows more precise and stable classifying performance. Through empirical analysis using data of job satisfaction of workplace reserve force commanders, we show that the grid search method helps us generate an optimal decision tree model that gives us hints for the improvement direction of labor conditions of Korean workplace reserve force commanders.

• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.161-170
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• 2012

As insurance consumers' needs have been diversified and subdivided, it is increasingly important to grasp their preferences by characteristics and properties. Even though changer in sales channels and marketing conditions of insurance require to analyze what consumers take serious views to purchase, it is difficult to devise marketing strategies since not many concrete studies have been done in this field. A questionnaire survey was carried out to learn detailed information about basic disposition and buying patterns of insurance consumers. Applying efficient statistical techniques and then utilizing a model for securing new customers, this study attempts to explore a plan for rapid growth and successive establishment of bancassurance.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.233-242
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• 2009

Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in crystalline rocks like gneiss, granite, and so on, a prediction model was developed by the use of a decision tree model. The classification standard of the selected prediction model is composed of the slope angle, the coefficient of permeability and the void ratio in the order. The computer program, SHAPP ver. 1.0 for prediction of slope hazards around an important national facilities using GIS technique and the developed model. To prove the developed prediction model and the computer program, the field data surveyed from Jumunjin, Gangneung city were compared with the prediction result in the same site. As the result of comparison, the real occurrence location of slope hazards was similar to the predicted section. Through the continuous study, the accuracy about prediction result of slope hazards will be upgraded and the computer program will be commonly used in practical.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.6
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• pp.1053-1069
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• 2020

This study examined the sales pattern relationship with respect to product attributes to propose sales forecasting for fashion products. We analyzed 537 SKU sales data of T-shirts in the domestic sports brand using SAS program. The sales pattern of fashion products fluctuated and were influenced by exogenous factors; therefore, we removed the influence of exogenous factors found to be price discounts and holiday effects as a result of regression analysis. In addition, it was difficult to predict sales using the sales patterns of the same product since fashion products were released as new products every year. Therefore, the forecasting model was proposed using sales patterns of related product attributes when attributes were considered descriptive variables. We classified sales patterns using K-means clustering in order to explain the relationship between sales patterns and product attributes along with creating a decision tree classifier using attributes as input and sales patterns as output. As a result, the sales patterns of T-shirts were clustered into six types that featured the characteristic shape of peak and slope. It was also associated with the combination of product attributes and their values in regards to the proposed sales pattern prediction model.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.177-190
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• 2021

Random forests is a popular method that improves the instability and accuracy of decision trees by ensembles. In contrast to increasing the accuracy, the ease of interpretation is sacrificed; hence, to compensate for this, variable importance is provided. The variable importance indicates which variable plays a role more importantly in constructing the random forests. However, when a predictor is correlated with other predictors, the variable importance of the existing importance algorithm may be distorted. The downward bias of correlated predictors may reduce the importance of truly important predictors. We propose a new algorithm remedying the downward bias of correlated predictors. The performance of the proposed algorithm is demonstrated by the simulated data and illustrated by the real data.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.109-118
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• 2023

Data-based analysis methods have become used more for estimating or predicting housing prices, and neural network models and decision trees in the field of big data are also widely used more and more. Neural network models are often evaluated to be superior to existing statistical models in terms of estimation or prediction accuracy. However, there is ambiguity in determining the input feature of the input layer of the neural network model, that is, the type and number of input features, and decision trees are sometimes used to overcome these disadvantages. In this paper, we evaluate the existing methods of using decision trees and propose the method of using decision trees to prioritize input feature selection in neural network models. This can be a complementary or combined analysis method of the neural network model and decision tree, and the validity was confirmed by applying the proposed method to house price estimation. Through several comparisons, it has been summarized that the selection of appropriate input characteristics according to priority can increase the estimation power of the model.

• Journal of Korean Public Health Nursing
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• v.37 no.1
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• pp.125-135
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• 2023

Purpose: The number of patients suffering from depression is rapidly increasing worldwide, and by 2030, it is expected to pose a severe social and economic burden. Reports suggest that approximately 30% of subjects with symptoms of depression do not attempt treatment. Therefore, predicting the characteristics of subjects with depressive symptoms who have not even attempted counseling treatment is essential to increase the participation rate for such treatment. This study intends to predict the participation rates for psychological counseling treatment for depression among subjects with depressive symptoms. Methods: This study used data from the 2021 Korea Community Health Survey (KCHS). Data analysis was carried out using a decision tree to design a model that predicted participation in psychological counseling for depression. Results: The results showed that subjects aged 65 to 74 had difficulty understanding the explanations of medical staff even though they did not have cognitive impairment. Only 11.1% of this group received psychological counseling, which was the lowest rate among the various age groups. Among the subjects, 62.4% of those aged 19-44 or 45-64, who had suicidal thoughts and attempted suicide, received psychological counseling and this was the highest rate among the age groups surveyed. Conclusion: The identification of people showing depressive symptoms is crucial for encouraging them to undertake treatment. Also, proper depression-oriented medical services should be developed and implemented for people with depressive symptoms who exhibit a blind spot towards attempting treatment.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.375-385
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• 2015

This study aims to identify geographical variations and factors that affect smoking rates. The data are collected from the Community Health Survey conducted between 2009 and 2011 by Korea Centers for Disease Control and Prevention and other government organizations. Correlation and multiple regression analysis were used to examine the factors influencing smoking rates. For the purpose of investigating regional variations, we employed a decision tree model. The study has found that the significant factors associated with geographical variations in the smoking rates were the rate of hazardous drinking, the completion rate of hypertension education, the experience rate of anti-smoking campaigns, stress awareness rate, hypertension prevalence, health insurance cost, diabetes prevalence, obesity rate, and strength training rate. Convergence-based analysis on geographical variations of the smoking rates is highly important when the regionally customized healthcare programs is implemented. In the future, it is necessary to develop effective program and customized approach for the regions of high smoking rates. Our study is expected to be used as meaningful data for the design of effective health care programs and assessments to lead effective non-smoking program.

• Journal of the military operations research society of Korea
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• v.33 no.2
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• pp.101-113
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• 2007

The period of military personnel service will be phased down by 2014 according to 'The law of National Defense Reformation' issued by the Ministry of National Defense. For this reason, the ROK army provides discrimination education to 'newly recruited privates' for more effective individual performance in the on-the-job training. For the training to be more effective, it would be essential to predict the degree of achievements by new privates in the training. Thus, we used data mining techniques to develop a classification model which classifies the new privates into one of two achievements groups, so that different skills of education are applied to each group. The target variable for this model is a binary variable, whose value can be either 'a group of general control' or 'a group of special control'. We developed four pure classification models using Neural Network, Decision Tree, Support Vector Machine and Naive Bayesian. We also built four hybrid models, each of which combines k-means clustering algorithm with one of these four mining technique. Experimental results demonstrated that the highest performance model was the hybrid model of k-means and Neural Network. We expect that various military education programs could be supported by these classification models for better educational performance.

• The Journal of Korean Association of Computer Education
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• v.22 no.1
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• pp.87-98
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• 2019

It is easy to assume that contradictions are logically incorrect or empty sets that have no solvability. This dilemma, which can not be done, is difficult to solve because it has to solve the contradiction hidden in it. Paradoxically, therefore, contradiction resolution has been viewed as an innovative and creative problem-solving. TRIZ, which analyzes the solution of the problem from the perspective of resolving contradictions, has been used for people rather than computers. The Butterfly model, which analyzes the problem from the perspective of solving the contradiction like TRIZ, analyzed the type of contradiction problem using symbolic logic. In order to apply an appropriate concrete solution strategy for a given contradiction problems, we designed the Butterfly algorithm based on decision making tree. We also developed a visualization tool based on Python tkInter to find concrete solution strategies for given contradiction problems. In order to verify the developed tool, the third grade students of middle school learned the Butterfly algorithm, analyzed the contradiction of the wooden support, and won the grand prize at an invention contest in search of a new solution. The Butterfly algorithm developed in this paper systematically reduces the solution space of contradictory problems in the beginning of problem solving and can help solve contradiction problems without trial and errors.