• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.173-176
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• 1997

In this paper, the classification performances of inductive learning methods are investigated using the credit rating data. The adopted classifiers are Multiple Discriminant Analysis (MDA), C4.5 of Quilan, Multi-Layer Perceptron (MLP) and Cascade Correlation Network (CCN). The data used in this analysis is obtained using the publicly announced rating reports from the three korean rating agencies. The performances of 4 classifiers are analyzed in term of prediction accuracy. The results show that no classifier is dominated by the other classifiers.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.277-286
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• 2012

For credit assessment models, the ROC curves evaluate the classification performance using two univariate cumulative distribution functions of the false positive rate and true positive rate. In this paper, it is extended to two bivariate normal distribution functions of default and non-default borrowers; in addition, the bivariate ROC curves are proposed to represent the joint cumulative distribution functions by making use of the linear function that passes though the mean vectors of two score random variables. We explore the classification performance based on these ROC curves obtained from various bivariate normal distributions, and analyze with the corresponding AUROC. The optimal threshold could be derived from the bivariate ROC curve using many well known classification criteria and it is possible to establish an optimal cut-off criteria of bivariate mixture distribution functions.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.47-61
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• 2001

Lazy loaming methods including CBR have relative advantages in comparison with eager loaming methods such as artificial neural networks and decision trees. However, they are very sensitive to irrelevant features. In other words, when there are irrelevant features, larry learning methods have difficulty in comparing cases. Therefore, their performance can be degraded significantly. To overcome this disadvantage, feature weighting methods for lazy loaming methods have been studied. Most of the existing researches, however, were focused on global feature weighting. In this research, we propose a new local feature weighting method, which we shall call CBDFW. CBDFW stores classification performance of randomly generated feature weight vectors. Then, given a new query case, CBDFW retrieves the successful feature weight vectors and designs a feature weight vector fur the query case. In the test on credit evaluation domain, CBDFW showed better classification accuracy when compared to the results of previous researches.

• Journal of Engineering Education Research
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• v.9 no.1
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• pp.5-19
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• 2006

The study aims to design and suggest criteria of the academic credit approval for those who obtain diverse qualifications. In order to achieve the objectives of this research, a Delphi survey was conducted. Consequently this study suggests the following, based on the argument that existing credit approval system covering all qualifications requires improvement especially due to limited function and efficiency. First, most respondents who took part in the management of academic credit bank system in the Korea Educational Development Institute stated that the current system is of limitation. Out of the all items, one was indicated as one not directly related the majoring area for the degree, three items were indicated as approved in the bachelor degree and two items were as approved in the associate degree. Second, occupational area for approving qualification items should be classified according to the KRIVET classification structure for national qualification.

• Asian Journal of Business Environment
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• v.8 no.1
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• pp.23-29
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• 2018

Purpose - The purpose of this research is to make a comparative assessment of People Credit Funds (PCFs) ranking in Vietnam between the Circular No. 42/2016/TT-NHNN dated December 20, 2016 with the Decision No. 14/2007/QD-NHNN dated 09/4/2007 issued by the Governor of the State Bank. Research design, data, and methodology - This study is mainly based on the Circular No. 42/2016/TT-NHNN dated December 20, 2016 and the Decision No. 14/2007/QD-NHNN dated 09/4/2007 issued by the Governor of the State Bank on PCFs ranking. Results - The study paper has shown positive changes in PCFs ranking in Vietnam in accordance with the Circular No. 42/2016/TT-NHNN, such as increasing Capital Adequacy Ratio (CAR), maintaining CAR, improving assets quality, developing indicators of governance, management and control capability. These changes have implications for the development and efficient performance of PCFs in Vietnam. Conclusions - The classification and evaluation of PCFs will contribute to its healthy development. These finding support PCFs to understand more about rating methodology, significance of rating system and the importance of improving their rating. PCFs in Vietnam desire to develop their business effectively, they need to understand exactly and comply fully with regulations related to their field of operations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1348-1375
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• 2018

Support Vector Machine (SVM) is a well-known machine learning classification algorithm, which has been widely applied to many data mining problems, with good accuracy. However, SVM classification speed decreases with increase in dataset size. Some applications, like video surveillance and intrusion detection, requires a classifier to be trained very quickly, and on large datasets. Hence, this paper introduces two filter-based instance selection techniques for optimizing SVM training speed. Fast classification is often achieved at the expense of classification accuracy, and some applications, such as phishing and spam email classifiers, are very sensitive to slight drop in classification accuracy. Hence, this paper also introduces two wrapper-based instance selection techniques for improving SVM predictive accuracy and training speed. The wrapper and filter based techniques are inspired by Cuckoo Search Algorithm and Bat Algorithm. The proposed techniques are validated on three popular e-fraud types: credit card fraud, spam email and phishing email. In addition, the proposed techniques are validated on 20 other datasets provided by UCI data repository. Moreover, statistical analysis is performed and experimental results reveals that the filter-based and wrapper-based techniques significantly improved SVM classification speed. Also, results reveal that the wrapper-based techniques improved SVM predictive accuracy in most cases.

• The Journal of Information Systems
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• v.16 no.1
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• pp.1-21
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• 2007

신용평점을 위한 부도예측의 분류 문제를 다루는데 있어서 통계적 판별분석 및 인공신경망 및 유전자알고리즘 등을 이용한 데이터 마이닝의 방법들이 일반적으로 고려되어왔다. 이 연구에서는 수리계획법을 응용하여 classification gap을 고려한 이단계 수리계획 접근방법을 신용평가에 적용하는 방법론을 제안하여 수리계획법을 통한 신용평가모형 구축의 가능성을 제시한다. 1단계에서는 선형계획법을 이용해서 대출 신청자에게 대출을 허가할 것 인지의 여부를 결정하게 되는 대출 심사 filtering으로의 적용단계이고, 2단계에서는 정수계획법을 이용하여 오분류 비용이 최소가 되도록 하는 판별점수를 찾는 과정으로 모형을 구성한다. 개인 대출 신청자의 데이터(German Credit Data)에 대하여 피셔의 선형 판별함수, 로지스틱 회귀모형 및 기존의 수리계획 기법들과의 비교를 통해서 제안된 모델의 성능을 평가한다. 이단계 수리계획 접근법의 평가 결과를 통하여 신용평가모형에의 적용가능성을 기존 통계적인 접근방법 및 수리계획 접근법과 비교하여 제시하고 있다.

• Journal of Engineering Education Research
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• v.16 no.4
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• pp.15-20
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• 2013

The result of the English test for freshmen showed wide difference in the ability of students, which means the graded class is strongly required. This paper describes the case study about the graded English class that was carried out for the freshman. Every freshman must take an English placement test from problems bank and were classified according to the result of the test. In order to resolve the dissatisfaction of the high level students, the statutes of my university for credit were modified. In order to analyze the effect of the graded class, evaluation test using similar problems was carried out at the end of the semester. The effect of the graded class was analyzed by using the paired samples t-test method and there was a meaningful performance improvement at the average score. Additional improvements in the method of classification and credit granted were made by analyzing the results of the evaluation test and survey.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.293-296
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• 2005

최근 우리나라는 가계신용의 급신장과 신용불량의 급증 등으로 개인 신용부문이 금융기관의 건전성 유지에 부정적인 영향을 미치고 있다. 이러한 잠재적 문제를 사전에 방지하기 위해 금융기관 등에서는 개인 신용평가에 대한 수요가 커지고 있는 실정이다. 주어진 데이터로부터의 반복적인 학습 과정을 거쳐 패턴을 분류하고 또한 모델과 학습 방법에 따라 입력변수와 목적변수의 속성이 연속형이나 이산형인 경우를 모두 다룰 수 있는 신경망 모델은 개개인의 다양하고 복잡한 데이터를 입력변수로 받아서 신용등급을 나누는데 우수한 능력을 보여줄 수 있다. 본 논문에서는 신경망 모델을 이용해 개인의 신용등급을 객관적이고 일률적으로 평가해서 등급을 나누어주는 알고리즘을 제안하고자 한다.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.4
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• pp.55-70
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• 2001

Artificial neural network(ANN) models have been widely used for the classification problems in business such as bankruptcy prediction, credit evaluation, etc. Although the application of ANN to classification of consumers＇ choice behavior is a promising research area, there have been only a few researches. In general, most of the researches have reported that the classification performance of the ANN models were better than conventional statistical model Because the survey data on consumer behavior may include much noise and missing data, ANN model will be more robust than conventional statistical models welch need various assumptions. The purpose of this paper is to study the potential of the ANN model for forecasting consumers＇ choice behavior based on survey data. The data was collected by questionnaires to the shoppers of department stores and discount stores. Then the correct classification rates of the ANN models for the training and test sample with that of multiple discriminant analysis(MDA) and logistic regression(Logit) model. The performance of the ANN models were betted than the performance of the MDA and Logit model with respect to correct classification rate. By using input variables identified as significant in the stepwise MDA, the performance of the ANN models were improved.