Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Evolutionary product unit based neural networks for regression
Neural Networks
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A systematic analysis of performance measures for classification tasks
Information Processing and Management: an International Journal
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
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This paper proposes a Radial Basis Function Neural Network (RBFNN) which reproduces different Radial Basis Functions (RBFs) by means of a real parameter q, named q-Gaussian RBFNN. The architecture, weights and node topology are learnt through a Hybrid Algorithm (HA) with the iRprop+ algorithm as the local improvement procedure. In order to test its overall performance, an experimental study with four gene microarray datasets with two classes taken from bioinformatic and biomedical domains is presented. The Fast Correlation-Based Filter (FCBF) was applied in order to identify salient expression genes from 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 the new input variables. The results confirm that the q-Gaussian RBFNN classifier leads to promising improvement on accuracy.