Comprehensible credit scoring models using rule extraction from support vector machines
نویسندگان
چکیده
منابع مشابه
Comprehensible credit scoring models using rule extraction from support vector machines
In recent years, Support Vector Machines (SVMs) were successfully applied to a wide range of applications. Their good performance is achieved by an implicit non-linear transformation of the original problem to a high-dimensional (possibly infinite) feature space in which a linear decision hyperplane is constructed that yields a nonlinear classifier in the input space. However, since the classif...
متن کاملRule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring
1 Department of Decision Sciences and Information Management, K.U.Leuven Naamsestraat 69, B-3000 Leuven, Belgium {David.Martens;Johan.Huysmans; Bart.Baesens;Jan.Vanthienen}@econ.kuleuven.be 2 School of Computing, National University of Singapore, 3 Science Drive 2, Singapore 117543, Singapore [email protected] 3 University of Southampton, School of Management, Highfield Southampton, SO17 1B...
متن کاملSupport Vector Machines for Credit Scoring
Quantitative methods to assess the creditworthiness of the loan applicants are vital for the profitability and the transparency of the lending business. With the total loan volumes typical for traditional financial institutions, even the slightest improvement in credit scoring models can translate into substantial additional profit. Yet for the regulatory reasons and due to the potential model ...
متن کاملEclectic Rule - Extraction from Support Vector Machines
superior performance compared to other machine learning techniques, especially in classification problems. Yet one limitation of SVMs is the lack of an explanation capability which is crucial in some applications, e.g. in the medical and security domains. In this paper, a novel approach for eclectic rule-extraction from support vector machines is presented. This approach utilizes the knowledge ...
متن کاملRule Extraction from Support Vector Machines
Support vector machines (SVMs) are learning systems based on the statistical learning theory, which are exhibiting good generalization ability on real data sets. Nevertheless, a possible limitation of SVM is that they generate black box models. In this work, a procedure for rule extraction from support vector machines is proposed: the SVM+Prototypes method. This method allows to give explanatio...
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ژورنال
عنوان ژورنال: European Journal of Operational Research
سال: 2007
ISSN: 0377-2217
DOI: 10.1016/j.ejor.2006.04.051