Cost-sensitive ensemble learning: a unifying framework

Over the years, a plethora of cost-sensitive methods have been proposed for learning on data when different types of misclassification errors incur different costs. Our contribution is a unifying framework that provides a comprehensive and insightful overview on cost-sensitive ensemble methods, pinpointing their differences and similarities via a fine-grained categorization. Our framework contains natural extensions and generalisations of ideas across methods, be it AdaBoost, Bagging or Random Forest, and as a result not only yields all methods known to date but also some not previously considered.publishedVersio

Autoencoders for strategic decision support

In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available. We introduce and extend the use of autoencoders to provide strategically relevant granular feedback. A first experiment indicates that experts are inconsistent in their decision making, highlighting the need for strategic decision support. Furthermore, using two large industry-provided human resources datasets, the proposed solution is evaluated in terms of ranking accuracy, synergy with human experts, and dimension-level feedback. This three-point scheme is validated using (a) synthetic data, (b) the perspective of data quality, (c) blind expert validation, and (d) transparent expert evaluation. Our study confirms several principal weaknesses of human decision-making and stresses the importance of synergy between a model and humans. Moreover, unsupervised learning and in particular the autoencoder are shown to be valuable tools for strategic decision-making

Instance-dependent cost-sensitive learning: do we really need it?

Traditionally, classification algorithms aim to minimize the number of errors. However, this approach can lead to sub-optimal results for the common case where the actual goal is to minimize the total cost of errors and not their number. To address this issue, a variety of cost-sensitive machine learning techniques has been suggested. Methods have been developed for dealing with both class- and instance-dependent costs. In this article, we ask whether we really need instance-dependent rather than class-dependent cost-sensitive learning? To this end, we compare the effects of training cost-sensitive classifiers with instance- and class-dependent costs in an extensive empirical evaluation using real-world data from a range of application areas. We find that using instance-dependent costs instead of class-dependent costs leads to improved performance for cost-sensitive performance measures, but worse performance for cost-insensitive metrics. These results confirm that instance-dependent methods are useful for many applications where the goal is to minimize costs