• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1361-1371
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• 2016

Quantile regression provides a variety of useful statistical information to examine how covariates influence the conditional quantile functions of a response variable. However, traditional quantile regression (which assume a linear model) is not appropriate when the relationship between the response and the covariates is a nonlinear. It is also necessary to conduct variable selection for high dimensional data or strongly correlated covariates. In this paper, we propose a penalized quantile regression tree model. The split rule of the proposed method is based on residual analysis, which has a negligible bias to select a split variable and reasonable computational cost. A simulation study and real data analysis are presented to demonstrate the satisfactory performance and usefulness of the proposed method.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.419-429
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• 2015

We investigate how the regional prevalence of hyperlipidemia is affected by health-related and socioeconomic factors with a special emphasis on geographic variations. We focus on the likelihood of hyperlipidemia as function of various region-specific attributes. We analysis a data set at the level of 249 small administrative districts collected from 2012 Korean Community Health Survey by Korea Centers for Disease Control and Prevention. To estimate, we use several methods including correlation analysis, multiple regression and decision tree model. We find that the average prevalence of hyperlipidemia in 249 small districts is 9.6% and its coefficient of variation is 28.3%. Prevalence of hyperlipidemia in continental and capital regions is higher than in southeast coastal regions. Further findings using decision tree model suggest that variations of hyperlipidemia prevalence between regions is more likely to be associated with rate of employee, level of stress, prevalence of hypertension, angina pectoris, and osteoarthritis in their regions.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.139-155
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• 2003

This paper presents the new methodology of analyzing and classifying patterns of customers in mobile telecommunication market to enhance the performance of predicting the credit information based on the decision tree and neural network. With the application of variance selection process from decision tree, the systemic process of defining input vector's value and the rule generation were developed. In point of customer management, this research analyzes current customers and produces the patterns of them so that the company can maintain good customer relationship and makes special management on the customer who has huh potential of getting out of contract in advance. The real implementation of proposed method shows that the predicted accuracy is higher than existing methods such as decision tree(CART, C4.5), regression, neural network and combined model(CART and NN).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.3
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• pp.518-523
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• 2006

Due to keen competition among companies, they have segmented customers and they are trying to offer specially targeted customer by means of the distinguished method. In accordance, data mining techniques are noted as the effective method that extracts useful information. This paper explores customer segmentation of the home study company using a hybrid decision tree and artificial neural network model. With the application of variance selection process from decision tree, the systemic process of defining input vector's value and the rule generation were developed. In point of customer management, this research analyzes current customers and produces the patterns of them so that the company can maintain good customer relationship. The case study shows that the predicted accuracy of the proposed model is higher than those of regression, decision tree (CART), artificial neural networks.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.135-140
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• 2007

이탈 고객 예측은 데이터 마이닝에서 다루는 주요한 문제 중에 하나이다. 이탈 고객 예측은 일종의 분류(classification) 문제로 의사결정나무추론, 로지스틱 회귀분석, 인공신경망 등의 기법이 많이 활용되어왔다. 일반적으로 이탈 고객 예측을 위한 모델은 고객의 인구통계학적 정보와 계약이나 거래 정보를 입력변수로 하여 이탈 여부를 목표변수로 보는 형태로 분류 모델을 생성하게 된다. 본 연구에서는 고객과의 지속적인 접촉으로 발생되는 추가적인 사건 정보를 활용하여 연관성 규칙을 생성하고 이 결과를 기존의 방식으로 생성된 분류 모델과 결합하는 이탈 고객 예측 방법을 제시한다. 제시한 방법의 유용성을 확인하기 위해서 특정 국내 신용카드사의 실제 데이터를 활용하여 실험을 수행하였다. 실험 결과 제시된 방법이 기존의 전통적인 분류 모델에 비해서 향상된 성능을 보이는 것을 확인할 수 있었다. 제시된 예측 방법의 장점은 기존의 이탈 예측을 위한 입력 변수들 이외에 고객과 회사간의 접촉을 통해서 생성된 동적 정보들을 통합적으로 활용하여 예측 정확도를 높이고 실시간으로 이탈 확률을 갱신할 수 있다는 점이다.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.1-8
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• 2010

This study tested the applicability of a Data mining tool in the analysis of massive National Pension data for the purpose of developing fraud pension payment prediction model. This study is identified significant variables for fraud pension payment through the statistical analysis process and developed prediction models using data mining methodology.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.113-124
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• 2001

CHAID, logistic regression, bagging trees, and bagging trees are compared on SAS artificial data set as HMEQ in terms of classification accuracy and training time. In error rates, bagging trees is at the top, although its run time is slower than those of others. The run time of logistic regression is best among given models, but there is no uniformly efficient model satisfied in both criteria.

• 한국벤처창업학회:학술대회논문집
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• 2021.11a
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• pp.185-187
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• 2021

관리종목 지정 제도는 상장 기업 내 기업의 부실화를 경고하여 기업에게는 회생 기회를 주고, 투자자들에게는 투자 위험을 경고하기 위한 시장규제 제도이다. 본 연구는 관리종목과 비관리종목의 기업의 재무 데이터를 표본으로 하여 관리종목 지정 예측에 대한 연구를 진행하였다. 분석에 쓰인 분석 방법은 로지스틱 회귀분석, 의사결정나무, 서포트 벡터 머신, 소프트 보팅, 랜덤 포레스트, LightGBM이며 분류 정확도가 82.73%인 LightGBM이 가장 우수한 예측 모형이었으며 분류 정확도가 가장 낮은 예측 모형은 정확도가 71.94%인 의사결정나무였다. 대체적으로 앙상블을 이용한 학습 모형이 단일 학습 모형보다 예측 성능이 높았다.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.775-780
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• 2019

Techniques for predicting the future can be categorized into statistics-based and deep-run-based techniques. Among them, statistic-based techniques are widely used because simple and highly accurate. However, working-level officials have difficulty using many analytical techniques correctly. In this study, we compared the accuracy of prediction by applying multinomial logistic regression, decision tree, random forest, support vector machine, and Bayesian inference to marketing related data. The same marketing data was used, and analysis was conducted by using R. The prediction results of various techniques reflecting the data characteristics of the marketing field will be a good reference for practitioners.

• Journal of Service Research and Studies
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• v.3 no.1
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• pp.9-16
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• 2013

Data analysis is the universal classification techniques, which requires a lot of effort. It can be easily analyzed to understand the results. Decision tree which is developed by Breiman can be the most representative methods. There are two core contents in decision tree. One of the core content is to divide dimensional space of the independent variables repeatedly, Another is pruning using the data for evaluation. In classification problem, the response variables are categorical variables. It should be repeatedly splitting the dimension of the variable space into a multidimensional rectangular non overlapping share. Where the continuous variables, binary, or a scale of sequences, etc. varies. In this paper, we obtain the coefficients of precision, reproducibility and accuracy of the classification tree to classify and evaluate the performance of the new cases, and through experiments to evaluate.