| Research Abstract |
In financial activities such as investment and business finance, it is important to evaluate moderately credit risk against business failure of enterprises. The aim of this research is to construct a system for evaluating credit risk of enterprises using computational intelligence and for managing the risk to get as much profit as possible under some allowable risk by virtue,of multiple criteria decision making.
Firstly, support vector machines(SVMs) were applied to evaluate credit risk of enterprises on the basis of qualitative and quantitative data sets. Those data sets for business failure have some unbalance : failure data are only a few percentage, and almost of all date are of nonfailure. In order to overcome this problem, SVMs were modified by using multi-objective programming and/or goal programming. As a result, the modified SVM showed a good classification ability for the category with extremely fewer elements. Moreover, the rough set theory was applied to extract simple and e
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xplicit rules from the obtained support vectors.
Secondly, dynamically adapting learning machines for the change of environment were developed. Learning machines can increase their ability by making incremental learning. If we make only incremental learning, however, the decision rule becomes more and more complex, which resluts in poor generalization. Therefore, it is needed to remove unnecessary(or obstacle) data under the present situation. This is called "forgetting". In this research were developed several methods for forgetting not only in a passive manner in which the influence of data decreases over time but also in an active way in which unnecessary(obstacle) data are found and removed actively.
If we only avoid risk, we can not make an active financial activities, because every financial activitiy has some risk. Finally, therefore, data envelopment analysis(DBA) was applied to evaluate the efficiency of decision unit in order to get as much profit as possible under some allowable risk. Since the conventional DEA is based on the convex hull of data set taking into account the linear value judgment, it can not be applied to problems under nonlinear value judgment. A generalized DEA was developed to attempt to measure the efficiency of decision unit under several kinds of nonlinear value judgments. The effectiveness of the generalized DEA was proved through several examples. Less
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