Multiple comparison procedures
Multiple comparison procedures
Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Learning and generalization in radial basis function networks
Neural Computation
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Selection of relevant genes in cancer diagnosis based on their prediction accuracy
Artificial Intelligence in Medicine
Selecting differentially expressed genes using minimum probability of classification error
Journal of Biomedical Informatics
Reliability of Cross-Validation for SVMs in High-Dimensional, Low Sample Size Scenarios
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Evolutionary product-unit neural networks classifiers
Neurocomputing
On the effects of dimensionality on data analysis with neural networks
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Computers in Biology and Medicine
Information Sciences: an International Journal
A novel hybrid feature selection method for microarray data analysis
Applied Soft Computing
Analysis of gene microarray data in a soft computing framework
Applied Soft Computing
Robust radial basis function neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoothly reproduce different RBFs by changing a real parameter @t. In addition, the mixed use of different RBF shapes in only one RBFNN is allowed. Generalized Radial Basis Function (GRBF) is based on Generalized Gaussian Distribution (GGD), which adds a shape parameter, @t, to standard Gaussian Distribution. Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers. The feasibility and benefits of the approach are demonstrated by means of six gene microarray classification problems taken from bioinformatic and biomedical domains. Three filters were applied: Fast Correlation-Based Filter (FCBF), Best Incremental Ranked Subset (BIRS), and Best Agglomerative Ranked Subset (BARS); this was done in order to identify salient expression genes from among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as new input variables. The results confirm that the GRBFNN classifier leads to a promising improvement in accuracy.