The nature of statistical learning theory
The nature of statistical learning theory
RBF Network Methods for Face Detection and Attentional Frames
Neural Processing Letters
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Machine Learning
Performance Evaluation of GAP-RBF Network in Channel Equalization
Neural Processing Letters
Adapting RBF Neural Networks to Multi-Instance Learning
Neural Processing Letters
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
High-dimensional Data Analysis: From Optimal Metrics to Feature Selection
High-dimensional Data Analysis: From Optimal Metrics to Feature Selection
Ml-rbf: RBF Neural Networks for Multi-Label Learning
Neural Processing Letters
A Lamarckian Hybrid of Differential Evolution and Conjugate Gradients for Neural Network Training
Neural Processing Letters
Sparse RBF Networks with Multi-kernels
Neural Processing Letters
Classification by evolutionary generalised radial basis functions
International Journal of Hybrid Intelligent Systems - Advances in Intelligent Agent Systems
A dynamic over-sampling procedure based on sensitivity for multi-class problems
Pattern Recognition
Accelerating FCM neural network classifier using graphics processing units with CUDA
Applied Intelligence
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Gene expression detection is a key bioinformatic problem which has been tackled as a classification problem of microarray gene expression, obtained by the light reflection analysis of genomic material. A typical microarray dataset may contain thousands of genes but only a small number of patterns (often less than two hundred). When the dataset presents these kinds of characteristics, state-of-the-art classification models show a high lack of performance. A two-stage algorithm has been proposed to successfully address the problem of microarray classification. In the first stage, two filter algorithms identify salient expression genes from thousands of genes. In the second stage, the proposed methodology is performed using selected gene subsets as new input variables. The methodology proposed is composed of a combination of Logistic Regression (LR) and Evolutionary Generalized Radial Basis Function (EGRBF) neural networks which have shown to be highly accurate in previous research in the modeling of high-dimensional patterns. Finally, the results obtained are contrasted with nonparametric statistical tests and confirm good synergy between EGRBF and LR models.