In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for...
This paper addresses the challenge of generating test sets that achieve functional coverage, in the absence of a complete specification. The inductive testing technique works by p...
Neil Walkinshaw, Kirill Bogdanov, John Derrick, Ja...
Abstract—While scalarization approaches to multicriteria optimization become infeasible in the case of many objectives, for few objectives the benefits of populationbased method...
The computer model of visual attention derives an interest or saliency map from an input image in a process that encompasses several data combination steps. While several combinati...
We study cost-sensitive learning of decision trees that incorporate both test costs and misclassification costs. In particular, we first propose a lazy decision tree learning that ...