• Journal of the Korean Data and Information Science Society
• /
• v.7 no.2
• /
• pp.283-287
• /
• 1996

본 연구는 중풍에서의 한의학의 풍부한 임상자료들에 대한 객관적이고도 논리적인 자료처리방법 및 변증으로부터 증형을 추론할 수 있는 통계적 방법을 연구하고자 한다. 중풍 전문의에 의해 수집된 65명의 환자들의 임상자료로부터 다변량 자료 분석의 하나인 판별분석을 이용하여 증후로부터 증형을 판단할 수 있는 수리적 판별모형을 구축하였다. 구축된 모형은 중풍 전문가 시스템을 개발하기 위한 기초가 될 것이다.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2006.06a
• /
• pp.195-202
• /
• 2006

본 연구에서는 보다 효과적인 기업부도예측을 위하여, 동계적 방법과 인공지능 방법을 결합한 통합모형을 제시하였다. 이를 위하여 통계적인 모형 중에서 가장 널리 활용되고 있는 다변량 판별분석, 로지스틱 회귀분석과 인공 지능적인 방법으로서 최근 널리 사용되고 있는 인공신경망, 규칙유도기법, 베이지안 망의 5가지 방법론을 통합한 Voting with Performance & Weights from ANN(WP-ANN) 통합모형을 제시하였다. 실험결과, 본 연구에서 제안한 WP-ANN 통합모형은 다변량 판별분석, 로지스탁 회귀분석, 인공신경망, 규칙유도기법, 베이지안 망 등의 단일모형과 비교한 결과 가장 예측정확성이 유수한 것으로 나타났다. 따라서 본 연구를 통해 기업부도예측에 있어서 WP-ANN 통합모형이 기존의 모형들에 비해 우수한 예측정확성을 나타냄을 알 수 있었다.

• The KIPS Transactions:PartD
• /
• v.12D no.2 s.98
• /
• pp.211-218
• /
• 2005

This study presents an integrated data mining model for the credit evaluation of the customers of a capital company. Based on customer information and financing processes in capital market, we derived individual models from multi-layered perceptrons(MLP), multivariate discrimination analysis(MDA), and decision tree. Further, the results from the existing models were compared with the results from the integrated model using genetic algorithm. The integrated model presented by this study turned out to be superior to the existing models. This study contributes not only to verifying the existing individual models but also to overcoming the limitations of the existing approaches.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2001.06a
• /
• pp.359-368
• /
• 2001

본 연구에서는 부도예측용 인공신경망의 입력노드 선정을 위한 휴리스틱으로 연결강도분석 접근법을 제안한다. 연결강도분석은 학습이 끝난 인공신경망에서 입력노드와 은닉노드와 연결된 가중치의 절대값 즉, 연결강도를 분석하여 입력변수를 선정하는 접근법으로, 본 연구에서는 약체연결뉴론제거법, 강체연결뉴론선택법 그리고 이 두 기법을 통합한 통합 연결강도 모형을 제안하여 각각 의사결정 트리 및 다변량판별분석에 의해 선정된 입력변수를 이용한 인공신경망 모형과 예측율을 비교한다. 실험 결과 본 연구에서 제안하고 있는 방법론이 의사결정트리나 다다변량판별분석 기법 보다 높은 예측율을 보여 주었다. 특히 두 기법의 통합연결강도 모형의 경우에는 다른 단일 기법보다 높은 예측율을 보이고 있다.

• Journal of Intelligence and Information Systems
• /
• v.7 no.2
• /
• pp.19-33
• /
• 2001

Link weight analysis approach is suggested as a heuristic for selection of input nodes in artificial neural network for bankruptcy prediction. That is to analyze each input node\\\\`s link weight-absolute value of link weight between an input node and a hidden node in a well-trained neural network model. Prediction accuracy of three methods in this approach, -weak-linked-neurons elimination method, strong-linked-neurons selection method and integrated link weight model-is compared with that of decision tree and multivariate discrimination analysis. In result, the methods suggested in this study show higher accuracy than decision tree and multivariate discrimination analysis. Especially an integrated model has much higher accuracy than any individual models.

• Asia pacific journal of information systems
• /
• v.5 no.1
• /
• pp.36-51
• /
• 1995

본 논문에서는 비구조적인 의사결정문제를 효과적으로 해결하기 위하여 감독학습 인공신경망 모형과 비감독학습 인공신경망 모형을 결합한 하이브리드 인공신경망 모형인 HYNEN(HYbrid NEural Network) 모형을 제안한다. HYNEN모형은 주어진 자료를 클러스터화 하는 CNN(Clustering Neural Network)과 최종적인 출력을 제공하는 ONN(Output Neural Network)의 2단계로 구성되어 있다. 먼저 CNN에서는 주어진 자료로부터 적정한 퍼지규칙을 찾기 위하여 클러스터를 구성한다. 그리고 이러한 클러스터를 지식베이스로하여 ONN에서 최종적인 의사결정을 한다. CNN에서는 SOFM(Self Organizing Feature Map)과 LVQ(Learning Vector Quantization)를 클러스터를 만든 후 역전파학습 인공신경망 모형으로 이를 학습한다. ONN에서는 역전파학습 인공신경망 모형을 이용하여 각 클러스터의 내용을 학습한다. 제안된 HYNEN 모형을 우리나라 기업의 도산자료에 적용하여 그 결과를 다변량 판별분석법(MDA:Multivariate Discriminant Analysis)과 ACLS(Analog Concept Learning System) 퍼지 ARTMAP 그리고 기존의 역전파학습 인공신경망에 의한 실험결과와 비교하였다.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.10
• /
• pp.520-526
• /
• 2016

This study aims to develop a distress prediction model, in order to evaluate the distress prediction power for first-class hotels and to calculate the average financial ratio in the Seoul area by using the financial ratios of hotels in 2015. The sample data was collected from 19 first-class hotels in Seoul and the financial ratios extracted from 14 of these 19 hotels. The results show firstly that the seven financial ratios, viz. the current ratio, total borrowings and bonds payable to total assets, interest coverage ratio to operating income, operating income to sales, net income to stockholders' equity, ratio of cash flows from operating activities to sales and total assets turnover, enable the top-level corporations to be discriminated from the failed corporations and, secondly, by using these seven financial ratios, a discriminant function which classifies the corporations into top-level and failed ones is estimated by linear multiple discriminant analysis. The accuracy of prediction of this discriminant capability turned out to be 87.9%. The accuracy of the estimates obtained by discriminant analysis indicates that the distress prediction model's distress prediction power is 78.95%. According to the analysis results, hotel management groups which administrate low level corporations need to focus on the classification of these seven financial ratios. Furthermore, hotel corporations have very different financial structures and failure prediction indicators from other industries. In accordance with this finding, for the development of credit evaluation systems for such hotel corporations, there is a need for systems to be developed that reflect hotel corporations' financial features.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2005.05a
• /
• pp.1068-1071
• /
• 2005

최근 들어 기술을 담보로 하는 신용금융의 역할이 증대되면서 자금지원 대상기업의 기술평가 시스템 구축이 중요한 과제가 되고 있다. 국내에서는 기업 보유의 기술경영성과를 측정하여 한정된 자원의 효율적 배분을 위한 민간 투, 융자를 위한 기술신용평가모형'이 제시되었다 본 연구에서는 기술신용평가모델의 평가항목 타당성을 실증 분석한다. 모형의 항목 분류가 적절하게 되었는지를 검증하기 위하여 구조적 타당성을 평가하며 통계적 유의성을 검증하여 신뢰성을 평가한다. 구조적 타당성 검정을 위해 확인 요인분석을 수행하며 평가모형의 신뢰성을 검증하기 위해서는 다변량 통계방법 중의 하나인 판별분석을 수행한다. 본 연구는 기술개발 성공 및 부실발생의 예측력을 갖는 기술신용평가 시스템 구축을 위한 기초 자료로 활용될 수 있을 것이다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.11 no.3
• /
• pp.1-15
• /
• 2016

This study has analyzed predication capabilities leveraging multi-variate model, logistic regression model, and artificial neural network model based on financial information of medium-small sized companies list in KOSDAQ. 83 delisted companies from 2009 to 2012 and 83 normal companies, i.e. 166 firms in total were sampled for the analysis. Modelling with training data was mobilized for 100 companies inlcuding 50 delisted ones and 50 normal ones at random out of the 166 companies. The rest of samples, 66 companies, were used to verify accuracies of the models. Each model was designed by carrying out T-test with 79 financial ratios for the last 5 years and identifying 9 significant variables. T-test has shown that financial profitability variables were major variables to predict a financial risk at an early stage, and financial stability variables and financial cashflow variables were identified as additional significant variables at a later stage of insolvency. When predication capabilities of the models were compared, for training data, a logistic regression model exhibited the highest accuracy while for test data, the artificial neural networks model provided the most accurate results. There are differences between the previous researches and this study as follows. Firstly, this study considered a time-series aspect in light of the fact that failure proceeds gradually. Secondly, while previous studies constructed a multivariate discriminant model ignoring normality, this study has reviewed the regularity of the independent variables, and performed comparisons with the other models. Policy implications of this study is that the reliability for the disclosure documents is important because the simptoms of firm's fail woule be shown on financial statements according to this paper. Therefore institutional arragements for restraing moral laxity from accounting firms or its workers should be strengthened.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.18 no.4
• /
• pp.77-88
• /
• 2023

The purpose of this study is to confirm whether the current technology appraisal model for investment, which is designed to identify high-growth SMEs in sales, which is one of the characteristics of gazelle companies, has the possibility of expanding employment effects. For SMEs classified as technology investment adequate firms(TI1-TI6) through technology appraisal for investment between 2016 and 2018 were targeted. At this time, the employment effect was analyzed by dividing the absolute employment effect and the relative employment effect. As a result of the analysis, it was confirmed that the technology appraisal items for investment defined as innovation characteristics did not have significant explanatory power for the absolute employment effect. However, for the relative employment effect, among innovation characteristics, technicality(TC) was found to have significant explanatory power, and this is because the item appraised based on future growth potential. In particular, the relative employment effect is meaningful in terms of the actual employment effect, and the conclusion is drawn that the current technology appraisal model for investment is an appraisal model with the possibility of expansion in terms of employment effect.