CREDIT SCORING ADAPTIF MENGGUNAKAN KERNEL LEARNING METHODS

• Main Authors: , MUHAMAD RASHIF HILMI, , Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc.
• 格式: Theses and Dissertations NonPeerReviewed
• 出版: [Yogyakarta] : Universitas Gadjah Mada 2014
• 主題: ETD
• 在線閱讀: https://repository.ugm.ac.id/131859/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=72368
• 機構: Universitas Gadjah Mada

Credit scoring is a method based on statistical analysis that used to measure the amount of credit risk. The most popular methods of classification adopted in the credit scoring industry are linear discriminant analysis and logistic regression. However, the method has some limitations. Those methods require the selection of variables for logistic regression and also the data must follow a certain distribution for linear discriminant analysis. Based on that information, it is difficult to automate the process of data modeling occurs when the environment or a population changes. Kernel method is one of the solutions to these problems. This method does not require effort and variable selection can always converge to the optimal solutions and provide the same results without encountering numerical problems or losing information. It enables modelers to design a credit scoring process dynamically in practice where decision model can be updated and improved with the arrival of new information.