Original Contribution: Stacked generalization
Neural Networks
Using Feature Construction to Improve the Performance of Neural Networks
Management Science
Combining Regularized Neural Networks
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Expert Systems with Applications: An International Journal
Implementing automated diagnostic systems for breast cancer detection
Expert Systems with Applications: An International Journal
Multiclass support vector machines for diagnosis of erythemato-squamous diseases
Expert Systems with Applications: An International Journal
Issues in stacked generalization
Journal of Artificial Intelligence Research
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
A comparison of regression methods for remote tracking of Parkinson's disease progression
Expert Systems with Applications: An International Journal
Novel hybrid feature selection algorithms for diagnosing erythemato-squamous diseases
HIS'12 Proceedings of the First international conference on Health Information Science
Hi-index | 12.05 |
This paper illustrates the use of combined neural networks (CNNs) model to guide model selection for diagnosis of the erythemato-squamous diseases. The multilayer perceptron neural networks (MLPNNs) were also tested and benchmarked for their performance on the diagnosis of the erythemato-squamous diseases. The domain contained records of patients with known diagnosis. Given a training set of such records, the classifiers learned how to differentiate a new case in the domain. The first level networks were used to detect the six erythemato-squamous diseases when 34 features defining six disease indications were used as inputs. To improve diagnostic accuracy, the second level networks were trained using the outputs of the first level networks as input data. The CNN model achieved accuracy rates which were higher than that of the stand-alone neural network model (MLPNN).