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Reducing the number of features whilst maintaining an acceptable classification accuracy is a fundamental step in the process of constructing cancer predictive models. In this work, we introduce a novel hybrid (MI-LDA) feature selection approach for the diagnosis of ovarian cancer. This hybrid approach is embedded within a global optimization framework and offers a promising improvement on feature...
Feature Selection is the process of selecting a subset of relevant features (i.e. predictors) for use in the construction of predictive models. This paper proposes a hybrid feature selection approach to breast cancer diagnosis which combines a Genetic Algorithm (GA) with Mutual Information (MI) for selecting the best combination of cancer predictors, with maximal discriminative capability. The selected...
Here’s what we know for sure. On the afternoon of Friday, 3 October 2014, Pyrros A. Telionis got a telephone call from the U.S. government’s Defense Threat Reduction Agency (DTRA). He was sitting in a featureless cubicle in Blacksburg, Va., that would have made the cartoon office drudge Dilbert feel right at home. The voice on the phone was brisk and professional. And highly specific. Could Telionis...
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