Please use this identifier to cite or link to this item:
Title: Differential scorecards for binary and ordinal data
Authors: Silva,PFB
Jaime Cardoso
Issue Date: 2015
Abstract: Generalized additive models are well-known as a powerful and palatable predictive modelling technique. Scorecards, the discretized version of generalized additive models, are a long-established method in the industry, due to its balance between simplicity and performance. Scorecards are easy to apply and easy to understand. Moreover, in spite of their simplicity, scorecards can model nonlinear relationships between the inputs and the value to be predicted. In the scientific community, scorecards have been largely overlooked in favor of more recent models such as neural networks or support vector machines. In this paper, we address scorecard development, introducing a new formulation more suitable to support regularization. We tackle both the binary and the ordinal data classification problems. In both settings, the proposed methodology shows advantages when evaluated using real datasets.
metadata.dc.type: article
Appears in Collections:CTM - Articles in International Journals

Files in This Item:
File Description SizeFormat 
P-00G-X1C.pdf295.32 kBAdobe PDFThumbnail

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.