We give a review of various aspects of boosting, clarifying the issues through a few simple results, and relate our work and that of others to the minimax paradigm of statistics. ...
AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. It uses the cost of misclassifications to update the training distribution on successive boo...
Wei Fan, Salvatore J. Stolfo, Junxin Zhang, Philip...
Abstract. We report on our recent progress in developing an ensemble of classifiers based algorithm for addressing the missing feature problem. Inspired in part by the random subsp...
This paper discusses concept lattices and some of their applications in component library development and compiler optimizations. Ongoing work on concept-based userextensible simp...
Sibylle Schupp, D. P. Gregor, B. Osman, David R. M...
In order to understand AdaBoost’s dynamics, especially its ability to maximize margins, we derive an associated simplified nonlinear iterated map and analyze its behavior in lo...
Cynthia Rudin, Ingrid Daubechies, Robert E. Schapi...