Ranking of Brain Tumour Classifiers Using a Bayesian Approach
نویسندگان
چکیده
This study presents a ranking model for classification models using a Bayesian perspective. This ranking framework is able to evaluate the performance of the models to be compared when they are inferred from different sets of data. It also takes into account the performance obtained with samples not used during the training of the models. Besides, this ranking model assigns a prior to each model based on a measure of similarity of the training data to a test case. An evaluation based on ranking brain tumour classifiers is presented. These classifiers based on multilayer perceptrons are trained with single voxel (SV) H magnetic resonance spectroscopy (MRS) signals at 1.5T at short echo time (TE, 2032ms) following a multiproject multicenter evaluation approach. We demonstrate that such a framework can be effectively applied to the real problem of selecting classifiers for brain tumour classification.
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