Stefan Lessmann

TL;DR: A framework for comparative software defect prediction experiments is proposed and applied in a large-scale empirical comparison of 22 classifiers over 10 public domain data sets from the NASA Metrics Data repository, showing an appealing degree of predictive accuracy, which supports the view that metric-based classification is useful.

TL;DR: The study of Baesens et al. (2003) is updated and several novel classification algorithms to the state-of-the-art in credit scoring are compared, providing an independent assessment of recent scoring methods and offering a new baseline to which future approaches can be compared.

TL;DR: Investigating the influence of different preprocessing techniques of attribute scaling, sampling, coding of categorical as well as coding of continuous attributes on the classifier performance of decision trees, neural networks and support vector machines provides empirical evidence that data preprocessing has a significant impact on predictive accuracy.

TL;DR: Empirical results suggest that LSTM outperforms a large number of alternative methods with substantial margin and an average forecast skill of 52.2% over the persistence model, suggesting that the technique is a promising technique, which deserves a place in forecasters’ toolbox.

TL;DR: This study benchmarks an optimized logit model against eight state-of-the-art data mining techniques that use standard input data, including real-world cross-sectional data from a large European telecommunication provider and finds effective data preparation improves AUC up to 14.5% and top decile lift up to 34%.