Credit scoring using incremental learning algorithm for SVDD

Abstract

Support Vector Data Description (SVDD) has a limitation for dealing with a large dataset or online learning tasks. This work investigates the practice of credit scoring and proposes a new incremental learning algorithm for SVDD based on Karush-Kuhn-Tucker (KKT) conditions and convex hull. Convex hull and part of newly added samples which violates KKT conditions are treated as new training samples instead of previous support vector and entire new arrived samples. The proposed method can achieve comparable training time with traditional incremental learning algorithm for SVDD while have similar classification accuracy with original SVDD.