Applying kernel logistic regression in data mining to classify credit risk

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

Credit risk evaluation is an interesting and important data mining problem in financial analysis domain. This problem domain, do require estimable class probabilities as well as accurate classification method. One of classification methods in the kernel-machine techniques and data mining communities that allows non linear probabilistic classification, transparent reasoning, and competitive discriminative ability is Kernel Logistic Regression. Kernel Logistic Regression model is a kernelized version of Logistic Regression, which well known classification method in the field of statistical learning. The parameters of kernel model are given by the solution of a convex optimization problem, that can be found using the efficient Iteratively Re-weighted Least Squares (IRLS) algorithm. In this paper, we investigated the classification performance of applying Kernel Logistic Regression to classify risk credit problem. The result demonstrated that Kernel Logistic Regression has good accuracy to evaluate credit risk, comparable with another well known kernel machine, Support Vector Machine.

Original languageEnglish
Title of host publicationProceedings - International Symposium on Information Technology 2008, ITSim
DOIs
Publication statusPublished - 2008
Externally publishedYes
EventInternational Symposium on Information Technology 2008, ITSim - Kuala Lumpur, Malaysia
Duration: 26 Aug 200829 Aug 2008

Publication series

NameProceedings - International Symposium on Information Technology 2008, ITSim
Volume2

Conference

ConferenceInternational Symposium on Information Technology 2008, ITSim
Country/TerritoryMalaysia
CityKuala Lumpur
Period26/08/0829/08/08

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