Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance.
Neural Netw
; 21(2-3): 427-36, 2008.
Article
em En
| MEDLINE
| ID: mdl-18272329
ABSTRACT
This study investigates the effect of class imbalance in training data when developing neural network classifiers for computer-aided medical diagnosis. The investigation is performed in the presence of other characteristics that are typical among medical data, namely small training sample size, large number of features, and correlations between features. Two methods of neural network training are explored classical backpropagation (BP) and particle swarm optimization (PSO) with clinically relevant training criteria. An experimental study is performed using simulated data and the conclusions are further validated on real clinical data for breast cancer diagnosis. The results show that classifier performance deteriorates with even modest class imbalance in the training data. Further, it is shown that BP is generally preferable over PSO for imbalanced training data especially with small data sample and large number of features. Finally, it is shown that there is no clear preference between oversampling and no compensation approach and some guidance is provided regarding a proper selection.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
/
Redes Neurais de Computação
/
Tomada de Decisões
/
Retroalimentação
Tipo de estudo:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Neural Netw
Assunto da revista:
NEUROLOGIA
Ano de publicação:
2008
Tipo de documento:
Article
País de afiliação:
Estados Unidos