Paper Info
Title
Ranking of Brain Tumour Classifiers Using a Bayesian Approach
Abstract
This study presents a ranking for classifers 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 on samples not used during the training of the classifiers. Besides, this ranking assigns a prior to each model based on a measure of similarity of the training data to a test case. An evaluation consisting of ranking brain tumour classifiers is presented. These multilayer perceptron classifiers are trained with 1H magnetic resonance spectroscopy (MRS) signals 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.
Year
DOI
Venue
2009
10.1007/978-3-642-02478-8_126
IWANN (1)
Keywords
Field
DocType
magnetic resonance spectroscopy,training data,bayesian approach,multiproject multicenter evaluation approach,ranking brain tumour classifier,multilayer perceptron classifier,different set,brain tumour,real problem,ranking framework,bayesian perspective,brain tumour classification,difference set,multilayer perceptron
Training set,Ranking,Pattern recognition,Computer science,Random subspace method,Decision support system,Tumour classification,Multilayer perceptron,Artificial intelligence,Machine learning,Bayesian probability
Conference
Volume
ISSN
Citations 
5517
0302-9743
2
PageRank 
References 
Authors
0.39
3
11
Name
Order
Citations
PageRank
Javier Vicente1535.45
Juan Miguel García-Gómez220.39
Salvador Tortajada393.64
Alfredo T. Navarro420.39
Franklyn A. Howe5202.88
Andrew Peet611110.48
Margarida Julià-sapé7908.13
Bernardo Celda8897.23
Pieter Wesseling940.77
Magí Lluch i Ariet101088.75
Montserrat Robles11106458.83