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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)
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)
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)
In this article, we begin by presenting OMeD, a medical decision support system, and argue for its value over purely probabilistic approaches that reason about patients for time-critical decision scenarios. We then progress to present Holmes, a Hybrid Ontological and Learning MEdical System which supports decision making about patient treatment. This system(More)
Model-building under the supervised learning domain potentially face a dual learning problem of identifying both the parameters of the model and the subset of (domain) attributes necessary to support the model: or an embedded as opposed to wrapper or filter based design. Genetic Programming (GP) has always addressed this dual problem, however, further(More)
The 3 × 3 Rubik cube represents a potential benchmark for temporal sequence learning under a discrete application domain with multiple actions. Challenging aspects of the problem domain include the large state space and a requirement to learn invariances relative to the specific colours present. The latter element of the domain making it difficult to evolve(More)
This paper argues that one of the most important decisions in designing and deploying censorship resistance systems is whether one set of system options should be selected (the best), or whether there should be several sets of good ones. We model the problem of choosing these options as a cat-and-mouse game and show that the best strategy depends on the(More)
We present an empirical analysis of the effects of incorporating novelty-based fitness (phenotypic behavioral diversity) into Genetic Programming with respect to training, test and generalization performance. Three novelty-based approaches are considered: novelty comparison against a finite archive of behavioral archetypes, novelty comparison against all(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 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)