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We report on our recent progress in developing an ensemble of clas-sifiers based algorithm for addressing the missing feature problem. Inspired in part by the random subspace method, and in part by an AdaBoost type distribution update rule for creating a sequence of classifiers, the proposed algorithm generates an ensemble of classifiers, each trained on a(More)
Keywords: Missing data Missing features Ensemble of classifiers Random subspace method a b s t r a c t We introduce Learn + + .MF, an ensemble-of-classifiers based algorithm that employs random subspace selection to address the missing feature problem in supervised classification. Unlike most established approaches, Learn + + .MF does not replace missing(More)
Analysis of DNA sequences isolated directly from the environment, known as metagenomics, produces a large quantity of genome fragments that need to be classified into specific taxa. Most composition-based classification methods use all features instead of a subset of features that may maximize classifier accuracy. We show that feature selection methods can(More)
Strongyloidiasis is a common parasitic disease in tropical regions of the world. Infection with Strongyloides stercoralis usually remains asymptomatic with peripheral eosinophilia and uncontrolled growth. Consequently, immunocompromised individuals are at a higher risk of complications of this disease. We present a case of an immunocompetent patient whose(More)
We present a case of a 42-year-old female who presented to our institution with a small bowel obstruction and had emergent surgical decompression. Thirteen days postoperatively, the patient became tachycardic and had worsening epigastric pain. Electrocardiogram showed significant ST-segment elevations in leads II, III, aVF, and V3-V6, suggesting the(More)
We discuss an ensemble-of-classifiers based algorithm for the missing feature problem. The proposed approach is inspired in part by the random subspace method, and in part by the incremental learning algorithm, Learn++. The premise is to generate an adequately large number of classifiers, each trained on a different and random combination of features, drawn(More)
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