The Experts below are selected from a list of 17058 Experts worldwide ranked by ideXlab platform
Chunling Chuang - One of the best experts on this subject based on the ideXlab platform.
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constructing a reassigning credit Scoring Model
Expert Systems With Applications, 2009Co-Authors: Chunling ChuangAbstract:Credit Scoring Model development became a very important issue as the credit industry has many competitions and bad debt problems. Therefore, most credit Scoring Models have been widely studied in the areas of statistics to improve the accuracy of credit Scoring Models during the past few years. In order to solve the classification and decrease the Type I error of credit Scoring Model, this paper presents a reassigning credit Scoring Model (RCSM) involving two stages. The classification stage is constructing an ANN-based credit Scoring Model, which classifies applicants with accepted (good) or rejected (bad) credits. The reassign stage is trying to reduce the Type I error by reassigning the rejected good credit applicants to the conditional accepted class by using the CBR-based classification technique. To demonstrate the effectiveness of proposed Model, RCSM is performed on a credit card dataset obtained from UCI repository. As the results indicated, the proposed Model not only proved more accurate credit Scoring than other four common used approaches, but also contributes to increase business revenue by decreasing the Type I and Type II error of Scoring system.
So Young Sohn - One of the best experts on this subject based on the ideXlab platform.
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technology credit Scoring Model with fuzzy logistic regression
Applied Soft Computing, 2016Co-Authors: So Young Sohn, Jin Hee YoonAbstract:We propose a technology credit Scoring Model based on fuzzy logistic regression.Fuzzy predictor, fuzzy binary responses with crisp coefficients are considered.Fuzzy least square method is used to estimate parameters.The performance of proposed fuzzy logistic regression Model is improved compared to the logistic regression. Technology credit Scoring Models have been used to screen loan applicant firms based on their technology. Typically a logistic regression Model is employed to relate the probability of a loan default of the firms with several evaluation attributes associated with technology. However, these attributes are evaluated in linguistic expressions represented by fuzzy number. Besides, the possibility of loan default can be described in verbal terms as well. To handle these fuzzy input and output data, we proposed a fuzzy credit Scoring Model that can be applied to predict the default possibility of loan for a firm that is approved based on its technology. The method of fuzzy logistic regression as an appropriate prediction approach for credit Scoring with fuzzy input and output was presented in this study. The performance of the Model is improved compared to that of typical logistic regression. This study is expected to contribute to practical utilization of the technology credit Scoring with linguistic evaluation attributes.
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updating a credit Scoring Model based on new attributes without realization of actual data
European Journal of Operational Research, 2014Co-Authors: Yong Han Ju, So Young SohnAbstract:Funding small and medium-sized enterprises (SMEs) to support technological innovation is critical for national competitiveness. Technology credit Scoring Models are required for the selection of appropriate funding beneficiaries. Typically, a technology credit-Scoring Model consists of several attributes and new Models must be derived every time these attributes are updated. However, it is not feasible to develop new Models until sufficient historical evaluation data based on these new attributes will have accumulated. In order to resolve this limitation, we suggest the framework to update the technology credit Scoring Model. This framework consists of ways to construct new technology credit-Scoring Model by comparing alternative scenarios for various relationships between existing and new attributes based on explanatory factor analysis, analysis of variance, and logistic regression. Our approach can contribute to find the optimal scenario for updating a Scoring Model.
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Behavior Scoring Model for coalition loyalty programs by using summary variables of transaction data
Expert Systems With Applications, 2013Co-Authors: So Young SohnAbstract:OKCashbag (OCB), the largest coalition loyalty program in Korea, offers a number of benefits such as sharing customer data with participating firms and cross-selling. There is great value in utilizing information pertaining to coalition loyal patrons. However, the size of transaction data is huge. We propose how to create necessary summary information by reducing the dimension of coalition transaction data. This information is then utilized to develop a behavior-Scoring Model. We expect that our study results can contribute to big data analysis for coalition loyalty program.
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cluster based dynamic Scoring Model
Expert Systems With Applications, 2007Co-Authors: So Young SohnAbstract:Abstract Importance of early prediction of bad creditors has been increasing extensively. In this paper, we propose a behavioral Scoring Model which dynamically accommodates the changes of borrowers’ characteristics after the loans are made. To increase the prediction efficiency, the data set is segmented into several clusters and the observation period is fractionized. The computational results showed that the proposed Model can replace the currently used static Model to minimize the loss due to bad creditors. The results of this study will help the loan lenders to protect themselves from the potential borrowers with high default risks in a timely manner.
I-fei Chen - One of the best experts on this subject based on the ideXlab platform.
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A Hybrid Recovery Scoring Model Using Multivariate Adaptive Regression Splines and Data Envelopment Analysis
2011 International Conference on Management and Service Science, 2011Co-Authors: I-fei ChenAbstract:This paper aims to devise a cardholder recovery Scoring Model which enables card issuers to identify creditworthy debtors recoverable from delinquency without misclassification risks. Taking advantage of integrating such highly performed classifiers as artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS) with a relative efficiency evaluation tool, data envelopment analysis (DEA), a classification Model with a more desired accuracy is built in the first phase for predicting delinquents' future credit status, and then DEA Model are employed in the second phase to verify the preceding-stage predicted results as well as gain managerial implications on the inefficient delinquents for improvement in the efficiency of card utilization.
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a two stage hybrid credit Scoring Model using artificial neural networks and multivariate adaptive regression splines
Expert Systems With Applications, 2005Co-Authors: I-fei ChenAbstract:The objective of the proposed study is to explore the performance of credit Scoring using a two-stage hybrid Modeling procedure with artificial neural networks and multivariate adaptive regression splines (MARS). The rationale under the analyses is firstly to use MARS in building the credit Scoring Model, the obtained significant variables are then served as the input nodes of the neural networks Model. To demonstrate the effectiveness and feasibility of the proposed Modeling procedure, credit Scoring tasks are performed on one bank housing loan dataset using cross-validation approach. As the results reveal, the proposed hybrid approach outperforms the results using discriminant analysis, logistic regression, artificial neural networks and MARS and hence provides an alternative in handling credit Scoring tasks.
Linus Ho - One of the best experts on this subject based on the ideXlab platform.
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a prognostic Scoring Model for the utility of induction chemotherapy prior to neoadjuvant chemoradiotherapy in esophageal cancer
Journal of Thoracic Oncology, 2017Co-Authors: Mian Xi, Zhongxing Liao, Weiye Deng, Cai Xu, Ritsuko Komaki, Mariela A Blum, Wayne L Hofstetter, Linus HoAbstract:Abstract Objectives The aim of this study was to identify patients with esophageal cancer who may benefit from induction chemotherapy (IC) before neoadjuvant chemoradiotherapy (nCRT) on the basis of a prognostic Scoring Model. Methods Between 1998 and 2015, 535 patients with esophageal cancer who underwent nCRT were included for analysis, including 218 patients who received IC before nCRT (IC group) and 317 patients who did not receive IC (non-IC group). A prognostic Scoring Model was developed to predict disease-free survival (DFS) on the basis of a Cox proportional hazards Model. Results The median follow-up time was 63.5 months (range 8.0–178.5) for survivors. The 5-year DFS rates were similar between the IC and non-IC groups (53.7% vs. 45.1%, p = 0.196). Multivariate analysis determined that histologic grade, tumor location, baseline positron emission tomography maximum standard uptake value, and lymph node size were independent prognostic factors for DFS. A prognostic Scoring system was constructed by using these four factors, with the total score ranging from 0 to 6.2. When the median value was used as a cutoff, low-risk (≤3.5) and high-risk (>3.5) groups were identified. In the high-risk group, patients who received IC had a nonsignificantly higher pathologic complete response rate ( p = 0.272) and a significantly better DFS ( p = 0.03) than patients who did not receive IC. After propensity score matching, the high-risk group demonstrated a significantly improved DFS with IC, a benefit that was not observed in the low-risk group. Conclusions On the basis of the prognostic Scoring Model, the addition of IC to nCRT may provide a DFS benefit in high-risk patients with a risk score higher than 3.5. Prospective validation is warranted.
Eunhee Choi - One of the best experts on this subject based on the ideXlab platform.
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development and validation of a novel prognostic Scoring Model for ischemic colitis
Diseases of The Colon & Rectum, 2010Co-Authors: Joo Won Chung, Jae Hee Cheon, Jae Jun Park, Eun Suk Jung, Eunhee ChoiAbstract:PURPOSE: This study was conducted to identify prognostic factors affecting the course of ischemic colitis and to develop a prognostic Scoring Model. METHODS: We analyzed medical records of consecutive patients with ischemic colitis treated between October 2002 and September 2008 at Severance Hospital, Seoul, Republic of Korea. Patients were excluded if results of endoscopy were unavailable. Patients were classified as having severe ischemic colitis on the basis of outcome (improvement delayed for more than 2 weeks, complications requiring surgery, or death). Univariate analyses and multivariate logistic regression analyses with backward stepwise selection were used to identify clinical, endoscopic, and laboratory variables associated with severe ischemic colitis. A novel prognostic Scoring Model was derived from the data, with probability of severe ischemic colitis and risk index determined for 8 risk groups based on independent risk factors identified by multivariate analyses. Predictive power was tested by means of 10-fold cross-validation, with area under the receiver operating characteristic curve representing discrimination accuracy. RESULTS: Analyzable data were available for 153 of 173 consecutive patients. Ischemic colitis was classified as severe in 20 patients. Multivariate analyses showed the following significant independent predictors of severe ischemic colitis: tachycardia (adjusted odds ratio = 4.6; 95% CI, 1.4-14.7), shock within 24 hours after admission (adjusted odds ratio = 6.5; 95% CI, 2.0-21.2), and endoscopic evidence of ulceration (adjusted odds ratio = 9.9; 95% CI, 2.0-48.8). Probability of severe ischemic colitis and risk index were 74 times higher for patients with all 3 risk factors (group 8) than for patients with none (group 1). Internal validation showed the area under the receiver operating characteristic curve to be 0.91 (95% confidence interval, 0.86-0.96). CONCLUSIONS: Endoscopic findings and instability of vital signs were associated with the disease course of ischemic colitis. A novel Scoring Model based on presence of tachycardia, shock within 24 hours after admission, and endoscopic evidence of ulceration provides a method of assessing patient prognosis and should be further validated.