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The problem of evolving binary classification models under increasingly unbalanced data sets is approached by proposing a strategy consisting of two components: Sub-sampling and 'robust' fitness function design. In particular, recent work in the wider machine learning literature has recognized that maintaining the original distribution of exemplars during(More)
Adopting a symbiotic model of evolution separates context for deploying an action from the action itself. Such a separation provides a mechanism for task decomposition in temporal sequence learning. Moreover, previously learned policies are taken to be synonymous with meta actions (actions that are themselves policies). Should solutions to the task not be(More)
Classification under large attribute spaces represents a dual learning problem in which attribute subspaces need to be identified at the same time as the classifier design is established. Embedded as opposed to filter or wrapper methodologies address both tasks simultaneously. The motivation for this work stems from the observation that team based(More)
In certain voting problems, a hidden ground truth is inferred by aggregating the opinions of an electorate. We propose a novel model of these underlying social interactions, and derive maximum likelihood estimators for the ground truth in these models, given the social network and votes. We also evaluate these new estimators, as well as existing ones, on a(More)
—Evolutionary methods for addressing the temporal sequence learning problem generally fall into policy search as opposed to value function optimization approaches. Various recent results have made the claim that the policy search approach is at best inefficient at solving episodic 'goal seeking' tasks i.e., tasks under which the reward is limited to(More)
Benchmarking of a team based model of Genetic Programming demonstrates that the naturally embedded style of feature selection is usefully extended by the teaming metaphor to provide solutions in terms of exceptionally low attribute counts. To take this concept to its logical conclusion the teaming model must be able to build teams with a non-overlapping(More)
Modern medical decision making systems require users to manually collect and process information from distributed and heterogeneous repositories to facilitate the decision making process. There are many factors (such as time, volume of information and technical ability) that can potentially compromise the quality of decisions made for patients. In this work(More)
In certain voting problems, a central authority must infer a hidden ground truth by aggregating the opinions of an electorate. When individual assessments are drawn i.i.d. and are correct with probability p > 0.5, aggregating enough votes will yield the ground truth with high probability. However, in reality voters' opinions are often influenced by those of(More)