Differential scorecards for binary and ordinal data

dc.contributor.author Silva,PFB en
dc.contributor.author Jaime Cardoso en
dc.date.accessioned 2018-01-14T21:01:10Z
dc.date.available 2018-01-14T21:01:10Z
dc.date.issued 2015 en
dc.description.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. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/6086
dc.identifier.uri http://dx.doi.org/10.3233/ida-150788 en
dc.language eng en
dc.relation 3889 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Differential scorecards for binary and ordinal data en
dc.type article en
dc.type Publication en
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