CREDIT SCORING ADAPTIF MENGGUNAKAN KERNEL LEARNING METHODS
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...
| Main Authors: | , |
|---|---|
| Format: | Thesis |
| Published: |
[Yogyakarta] : Universitas Gadjah Mada
2014
|
| Subjects: |
| Summary: | 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. |
|---|