Various tasks in decision making and decision support require selecting a preferred subset of items from a given set of feasible items. Recent work in this area considered methods...
Maxim Binshtok, Ronen I. Brafman, Solomon Eyal Shi...
We introduce a method to deal with the problem of learning from imbalanced data sets, where examples of one class significantly outnumber examples of other classes. Our method sel...
This paper develops a new class of algorithms for signal recovery in the distributed compressive sensing (DCS) framework. DCS exploits both intra-signal and inter-signal correlati...
Stephen R. Schnelle, Jason N. Laska, Chinmay Hegde...
Many real problems can be modelled as robust shortest path problems on digraphs with interval costs, where intervals represent uncertainty about real costs and a robust path is not...
For a presented case, a Bayesian network classifier in essence computes a posterior probability distribution over its class variable. Based upon this distribution, the classifier...
Linda C. van der Gaag, Silja Renooij, Wilma Steene...