TY - GEN
T1 - Ranking of brain tumour classifiers using a bayesian approach
AU - Vicente, Javier
AU - García-Gómez, Juan Miguel
AU - Tortajada, Salvador
AU - Navarro, Alfredo T.
AU - Howe, Franklyn A.
AU - Peet, Andrew C.
AU - Julià-Sapé, Margarida
AU - Celda, Bernardo
AU - Wesseling, Pieter
AU - Lluch-Ariet, Magí
AU - Robles, Montserrat
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=68749104989&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-02478-8_126
DO - 10.1007/978-3-642-02478-8_126
M3 - Conference contribution
AN - SCOPUS:68749104989
SN - 3642024777
SN - 9783642024771
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1005
EP - 1012
BT - Bio-Inspired Systems
T2 - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009
Y2 - 10 June 2009 through 12 June 2009
ER -