Operations Research Proceedings 2014: Selected Papers of the by Marco Lübbecke, Arie M.C.A. Koster, Peter Letmathe, Reinhard

By Marco Lübbecke, Arie M.C.A. Koster, Peter Letmathe, Reinhard Madlener, Britta Peis, Grit Walther

This booklet encompasses a collection of refereed papers offered on the "International convention on Operations examine (OR 2014)", which happened at RWTH Aachen college, Germany, September 2-5, 2014. greater than 800 scientists and scholars from forty seven nations attended OR 2014 and awarded greater than 500 papers in parallel topical streams, in addition to exact award periods. The subject of the convention and its court cases is "Business Analytics and Optimization".

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Math. 99, 39–54 (2000) 8. : On the complexity of approximating k-set packing. Comput. Complex. 15(1), 20–29 (2006) 9. : The Hypergraph Assignment Problem. D. Thesis, TU Berlin (2014) On Class Imbalance Correction for Classification Algorithms in Credit Scoring Bernd Bischl, Tobias Kühn and Gero Szepannek Abstract Credit scoring is often modeled as a binary classification task where defaults rarely occur and the classes generally are highly unbalanced. Although many new algorithms have been proposed in the recent past to mitigate this specific problem, the aspect of class imbalance is still underrepresented in research despite its great relevance for many business applications.

23–31. Springer, Heidelberg (2014) 6. : mlr: Machine Learning in R. 0. org/package=mlr (2014) 7. : An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Syst. Appl. 39(3), 3446–3453 (2012) On Class Imbalance Correction for Classification Algorithms in Credit Scoring 43 8. : SMOTE: synthetic minority oversampling technique. J. Artif. Intell. Res. 16, 321–357 (2002) 9. : Instance sampling in credit scoring: an empirical study of sample size and balancing.

For all set-ups (except the baseline) tuning is performed via iterated F-racing and a budget of 400 evaluations. During the inner resampling for tuning (in each racing step) we use 80 % of the observations for training and 20 % for testing. 90 Rate In comparison AUC of the same learner without tuning or imbalance correction (Base), with tuning of hyperparameters only (Tuning),—and class weights (Weights)—and the best sampling method (Sampling). 90 satellite image IR vehicle Data Table 4 AUC of the best algorithm (bold) and learner per dataset On Class Imbalance Correction for Classification Algorithms in Credit Scoring 41 42 B.

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