Title :
Sparse Maximum Margin Logistic Regression for Credit Scoring
Author :
Patra, Sabyasachi ; Shanker, Kripa ; Kundu, Debasis
Author_Institution :
Dept. of Ind. & Mgt. Eng., Indian Inst. of Technol., Kanpur
Abstract :
The objective of credit scoring model is to categorize the applicants as either accepted or rejected debtors prior to granting credit. A modified logistic loss function is proposed which can approximate hinge loss and therefore the resulting model, maximum margin logistic regression (MMLR), has the classification capability of support vector machine (SVM) with low computational cost. Finally, to classify credit applicants, an efficient algorithm is also described for MMLR based on epsilon-boosting which can provide sparse estimation of coefficients for better stability and interpretability.
Keywords :
finance; logistics; regression analysis; support vector machines; credit scoring model; epsilon boosting; logistic loss function; sparse maximum margin logistic regression; support vector machine; Computational efficiency; Data mining; Demography; Fasteners; Industrial training; Logistics; Risk management; Stability; Support vector machine classification; Support vector machines;
Conference_Titel :
Data Mining, 2008. ICDM '08. Eighth IEEE International Conference on
Conference_Location :
Pisa
Print_ISBN :
978-0-7695-3502-9
DOI :
10.1109/ICDM.2008.84