Application of the Bayesian MMSE estimator for classification error to gene expression microarray data
نویسندگان
چکیده
منابع مشابه
Application of the Bayesian MMSE estimator for classification error to gene expression microarray data
MOTIVATION With the development of high-throughput genomic and proteomic technologies, coupled with the inherent difficulties in obtaining large samples, biomedicine faces difficult small-sample classification issues, in particular, error estimation. Most popular error estimation methods are motivated by intuition rather than mathematical inference. A recently proposed error estimator based on ...
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The introduction of our paper discusses the leave-one-out and cross-validation error estimators. In our implementation of cross-validation, we use k = 5 folds and 5 repetitions, each with different partitions. The basic bootstrap zero estimator, ε̂b0, [3], [4] generates B bootstrap samples, each consisting of n equally-likely draws with replacement from the original sample of size n. Each bootst...
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The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been designed. Use of prior knowledge, in the form of a prior distribution on an ...
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ژورنال
عنوان ژورنال: Bioinformatics
سال: 2011
ISSN: 1460-2059,1367-4803
DOI: 10.1093/bioinformatics/btr272