Support vector machines for classifying and describing credit applicants: detecting typical and critical regions

DOI10.1057/PALGRAVE.JORS.2602023zbMath1095.90062OpenAlexW2035908274MaRDI QIDQ5694074

Ralf Stecking, Klaus B. Schebesch

Publication date: 29 September 2005

Published in: Journal of the Operational Research Society (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1057/palgrave.jors.2602023

zbMATH Keywords

classificationheuristicsquadratic optimizationforecastingfinancesupport vector machinesbanking

Mathematics Subject Classification ID

Management decision making, including multiple objectives (90B50) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (7)

Recent developments in consumer credit risk assessment ⋮ Adaptive credit scoring with kernel learning methods ⋮ Variable selection in classification model via quadratic programming ⋮ Weight-selected attribute bagging for credit scoring ⋮ Factor models for asset returns based on transformed factors ⋮ Credit scoring using support vector machines with direct search for parameters selection ⋮ A classification spline machine for building a credit scorecard

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