A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Hybrid algorithms with instance-based classification
ECML'05 Proceedings of the 16th European conference on Machine Learning
MML inference of oblique decision trees
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
A New Expert System for Diagnosis of Lung Cancer: GDA--LS_SVM
Journal of Medical Systems
The methodology of Dynamic Uncertain Causality Graph for intelligent diagnosis of vertigo
Computer Methods and Programs in Biomedicine
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An automatic system for the diagnosis of lung cancer has been proposed in this manuscript. The proposed method is based on combination of genetic algorithm (GA) for the feature selection and newly proposed approach, namely the extreme learning machines (ELM) for the classification of lung cancer data. The dimension of the feature space is reduced by the GA in this scheme and the effective features are selected in this way. The data are then fed to a fuzzy inference system (FIS) which is trained by the fuzzy extreme learning machines approach. The results on real data indicate that the proposed system is very effective in the diagnosis of lung cancer and can be used for clinical applications.