• Journal of Industrial Convergence
• /
• v.16 no.4
• /
• pp.33-46
• /
• 2018

Personal credit scoring is an effective tool for banks to properly guide decision profitably on granting loans. Recently, many classification algorithms and models are used in personal credit scoring. Personal credit scoring technology is usually divided into statistical method and non-statistical method. Statistical method includes linear regression, discriminate analysis, logistic regression, and decision tree, etc. Non-statistical method includes linear programming, neural network, genetic algorithm and support vector machine, etc. But for the development of the credit scoring model, there is no consistent conclusion to be drawn regarding which method is the best. In this paper, we will compare the performance of the most common scoring techniques such as logistic regression, neural network, and support vector machines using personal credit data of the financial institution in China. Specifically, we build three models respectively, classify the customers and compare analysis results. According to the results, support vector machine has better performance than logistic regression and neural networks.

• Journal of Korean Neurosurgical Society
• /
• v.67 no.2
• /
• pp.177-185
• /
• 2024

Objective : Delayed cerebral ischemia (DCI) is a major cause of disability in patients who survive aneurysmal subarachnoid hemorrhage (aSAH). Systemic inflammatory markers, such as peripheral leukocyte count and systemic immune-inflammatory index (SII) score, have been considered predictors of DCI in previous studies. This study aims to investigate which systemic biomarkers are significant predictors of DCI. Methods : We conducted a retrospective, observational, single-center study of 170 patients with SAH admitted between May 2018 and March 2022. We analyzed the patients' clinical and laboratory parameters within 1 hour and 3-4 and 5-7 days after admission. The DCI and non-DCI groups were compared. Variables showing statistical significance in the univariate logistic analysis (p<0.05) were entered into a multivariate regression model. Results : Hunt-Hess grade "4-5" at admission, modified Fisher scale grade "3-4" at admission, hydrocephalus, intraventricular hemorrhage, and infection showed statistical significance (p<0.05) on a univariate logistic regression. Lymphocyte and monocyte count at admission, SII scores and C-reactive protein levels on days 3-4, and leukocyte and neutrophil counts on days 5-7 exhibited statistical significance on the univariate logistic regression. Multivariate logistic regression analysis revealed that monocyte count at admission (odds ratio [OR], 1.64; 95% confidence interval [CI], 1.04-2.65; p=0.036) and SII score at days 3-4 (OR, 1.55; 95% CI, 1.02-2.47; p=0.049) were independent predictors of DCI. Conclusion : Monocyte count at admission and SII score 3-4 days after rupture are independent predictors of clinical deterioration caused by DCI after aSAH. Peripheral monocytosis may be the primer for the innate immune reaction, and the SII score at days 3-4 can promptly represent the propagated systemic immune reaction toward DCI.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.32 no.6B
• /
• pp.399-406
• /
• 2012

The objective of this research is to develop greenhouse gas generation models and estimation method of their parameters for solid waste landfills. Two models obtained by differentiating the Modified Gompertz and Logistic models were employed to evaluate two parameters of a first-order decay model, methane generation potential ($L_0$) and methane generation rate constant (k). The parameters were determined by the statistical comparison of predicted gas generation rate data using the two models and actual landfill gas collection data. The values of r-square obtained from regression analysis between two data showed that one model by differentiating the Modified Gompetz was 0.92 and the other model by differentiating the Logistic was 0.94. From this result, the estimation methods showed that $L_0$ and k values can be determined by regression analysis if landfill gas collection data are available. Also, new models based on two models obtained by differentiating the Modified Gompertz and Logistic models were developed to predict greenhouse gas generation from solid waste landfills that actual landfill generation data could not be available. They showed better prediction than LandGEM model. Frequency distribution of the ratio of Qcs (LFG collection system) to Q (prediction value) was used to evaluate the accuracy of the models. The new models showed higher accuracy than LandGEM model. Thus, it is concluded that the models developed in this research are suitable for the prediction of greenhouse gas generation from solid waste landfills.

• The Korean Journal of Applied Statistics
• /
• v.22 no.3
• /
• pp.531-539
• /
• 2009

Logistic regression is widely used as a datamining technique for the customer relationship management. The maximum likelihood estimator has highly inflated variance when multicollinearity exists among the regressors, and it is not robust against outliers. Thus we propose the robust principal components logistic regression to deal with both multicollinearity and outlier problem. A procedure is suggested for the selection of principal components, which is based on the condition index. When a condition index is larger than the cutoff value obtained from the model constructed on the basis of the conjoint analysis, the corresponding principal component is removed from the logistic model. In addition, we employ an algorithm for the robust estimation, which strives to dampen the effect of outliers by applying the appropriate weights and factors to the leverage points and vertical outliers identified by the V-mask type criterion. The Monte Carlo simulation results indicate that the proposed procedure yields higher rate of correct classification than the existing method.

• Journal of Distribution Science
• /
• v.12 no.11
• /
• pp.77-83
• /
• 2014

Purpose - The National Agricultural Cooperative Federation of Korea and National Fisheries Cooperative Federation of Korea have prosecuted both financial and retail businesses. As cooperatives are public institutions and receive government support, their sound management is required by the Financial Supervisory Service in Korea. This is mainly managed by CAEL, which is changed by CAMEL. However, NFFC's business section, managing the finance and retail businesses, is unified and evaluated; the CAEL model has an insufficient classification to evaluate the retail industry. First, there is discrimination power as regards CAEL. Although the retail business sector union can receive a higher rating on a CAEL model, defaults have often been reported. Therefore, a default prediction model is needed to support a CAEL model. As we have the default prediction model using a subdivision of indexes and statistical methods, it can be useful to have a prevention function through the estimation of the retail sector's default probability. Second, separating the difference between the finance and retail business sectors is necessary. Their businesses have different characteristics. Based on various management indexes that have been systematically managed by the National Fisheries Cooperative Federation of Korea, our model predicts retail default, and is better than the CAEL model in its failure prediction because it has various discriminative financial ratios reflecting the retail industry situation. Research design, data, and methodology - The model to predict retail default was presented using logistic analysis. To develop the predictive model, we use the retail financial statements of the NFCF. We consider 93 unions each year from 2006 to 2012 to select confident management indexes. We also adapted the statistical power analysis that is a t-test, logit analysis, AR (accuracy ratio), and AUROC (Area Under Receiver Operating Characteristic) analysis. Finally, through the multivariate logistic model, we show that it is excellent in its discrimination power and higher in its hit ratio for default prediction. We also evaluate its usefulness. Results - The statistical power analysis using the AR (AUROC) method on the short term model shows that the logistic model has excellent discrimination power, with 84.6%. Further, it is higher in its hit ratio for failure (prediction) of total model, at 94%, indicating that it is temporally stable and useful for evaluating the management status of retail institutions. Conclusions - This model is useful for evaluating the management status of retail union institutions. First, subdividing CAEL evaluation is required. The existing CAEL evaluation is underdeveloped, and discrimination power falls. Second, efforts to develop a varied and rational management index are continuously required. An index reflecting retail industry characteristics needs to be developed. However, extending this study will need the following. First, it will require a complementary default model reflecting size differences. Second, in the case of small and medium retail, it will need non-financial information. Therefore, it will be a hybrid default model reflecting financial and non-financial information.

• International Journal of Computer Science & Network Security
• /
• v.21 no.3
• /
• pp.245-256
• /
• 2021

Poor logistical infrastructure and agri-food supply chain management leads to significant food waste in logistic system. The concept of the sustainable value added agri-food chains requires defined approach to the analysis of the existing situation, possible improving strategies and also assessment of these changes impact on further development. The purpose of research is to provide scientific substantiation of theoretical and methodological principles and develop practical recommendations for the improvement of the agri-food logistics distribution system. A case study methodology is used in this article. The research framework is based on 4 steps: Value Stream Mapping (VSM), Gap and Process Analysis, Validation and Improvement Areas Definition and Imitation Modelling. This paper presents the appropriateness of LEAN logistics tools using, in particular, Value Stream Mapping (VSM) for minimizing logistic losses and Simulation Modeling of possible logistics distribution system improvement results. The algorithm of VSM analysis of the agri-food supply chain, which involves its optimization by implementing the principles of sustainable development at each stage, is proposed. The methodical approach to the analysis of possible ways for optimizing the operation of the logistics system of the agri-food distribution is developed. It involves the application of Value Stream Mapping, i.e. designing of stream maps of the creation of the added value in the agri-food supply chain for the current and future state based on the minimization of logistic losses. Simulation modeling of the investment project on time optimization in the agri-food supply chain and economic effect of proposed improvements in logistics product distribution system functioning at the level of the investigated agricultural enterprise has been determined. Improvement of logistics planning and coordination of operations in the supply chain and the innovative pre-cooling system proposed to be introduced have a 3-year payback period and almost 75-80% probability. Based on the conducted VSM analysis of losses in the agri-food supply chain, there have been determined the main points, where it is advisable to conduct optimization changes for the achievement of positive results and the significant economic effect from the proposed measures has been confirmed. In further studies, it is recommended to focus on identifying the synergistic effect of the agri-food supply chain optimization on the basis of sustainable development.

• Fire Science and Engineering
• /
• v.33 no.6
• /
• pp.72-79
• /
• 2019

Through this study, we developed a model for predicting chemical accidents lead to casualties. The model was derived from the logistic regression analysis model and applied to the variables affecting the accident. The accident data used in the model was analyzed by studying the statistics of past chemical accidents, and applying independent variables that were statistically significant through data analysis, such as the type of accident, cause, place of occurrence, status of casualties, and type of chemical accident that caused the casualties. A significance of p < 0.05 was applied. The model developed in this study is meaningful for the prevention of casualties caused by chemical accidents and the establishment of safety systems in the workplace. The analysis using the model found that the most influential factor in the occurrence of casualty in accidents was chemical explosions. Therefore, there is an urgent need to prepare countermeasures to prevent chemical accidents, specifically explosions, from occurring in the workplace.

• Journal of Korean Society of Forest Science
• /
• v.105 no.3
• /
• pp.360-365
• /
• 2016

The purpose of this study is to provide the basic information for the early enforcement and extension of the improvement project of management scale of private forest land by understanding the characteristics of forest owners, who have an influence on the participation of the project as one of the private forest management vitalization plans. To achieve this goal, a questionnaire survey targeting 373 forest owners was conducted and analyzed by Binary-Logistic Regression. The variables for binary-logistic regression included gender, age, academic ability, occupation, income, residence, purpose of forest ownership, and status of cooperative membership. As a result of the analysis, 267 forest owners (71.6%) of total 373 forest owners have the intention to participate in the scaling project for private forest management. The rest of forest owners (106 forest owners, 28.4%) would not be willing to participate in the project. As a result of binary-logistic regression, the most important variables, which have an impact on the participation of private forest management scale improvement project, are age, job and forest own purpose.

• The Journal of the Korea Contents Association
• /
• v.15 no.8
• /
• pp.366-374
• /
• 2015

This study investigated what factors would affect suicidal ideation of the people who have received public assistance. For the purpose, the survey results of the 7th year(2012) of Korea Welfare Panel Study, which were conducted by Korea Institute for health and Social Affairs and Social Welfare Research Institute of Seoul National University, were used for analysis. In order to figure out the level of influence on the suicidal ideation, a binary logistic regression analysis using a binary logistic model was used as an analysis method. As a result, it was found that when the subjects are middle school graduates, and if they are married, there are low suicidal ideation. Also, the higher their self-esteem is and the higher their satisfaction with public assistance, the lower there they have suicidal ideation. Furthermore, it was proved that if they have depression, or in middle age, they have high possibility of suicidal ideation. It was proved that satisfaction with public assistance also can have influence on the suicidal ideation of the poor class, not only physical and psychological factors. Therefore, measuring the satisfaction of the recipients with public assistance can be one of the significant factors that affects suicidal ideation.

• The KIPS Transactions:PartD
• /
• v.12D no.7 s.103
• /
• pp.1017-1022
• /
• 2005

There are many changes in the software requirements during the whole software life cycle. These changes require modification of the software, and it is important to keep software quality and stability while we are modifying the software. Refactoring is one of the technology to keep software quality and stability during the software modification; there are many researches related to automatic refactoring. In this paper, we propose three factors for Move Method which is one of the refactoring technique. We applied binomial logistic analysis to data which were extracted from sample program by each factor. The result of this process was very close to the result of manual analysis by program experts. Furthermore, we found that these factors have major roll to determine Position of a method, and these factors can be used as a basis of finding optimal position of a method.