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Given the large amount of data mining algorithms, their combinations (e.g. ensembles) and possible parameter settings, finding the most adequate method to analyze a new dataset becomes an ever more challenging task. This is because in many cases testing all possibly useful alternatives quickly becomes prohibitively expensive. In this paper we propose a(More)
The problem of selecting the best classification algorithm for a specific problem continues to be very relevant, especially since the number of classification algorithms keeps growing significantly. Testing all alternatives is not really a viable option: if we compare all pairs of algorithms, as is often advocated, the number of comparisons grows(More)
The pursuit of unhealthy behaviors, such as smoking or binge drinking, not only carries various downside risks, but also provides pleasure. A parsimonious model, used in the literature to explain the decision to pursue an unhealthy activity, represents that decision as a tradeoff between risks and benefits. We build on this literature by surveying a rural(More)