A Comparison of Feature Extraction and Selection Techniques

We have applied several dimensionality reduction techniques to data modelling using neural network architectures for classification using a number of data sets. The reduction methods considered include both linear and non linear forms of principal components analysis, genetic algorithms and sensitivity analysis. The results of each were used as inputs to several types of neural network architecture, specifically the performance of Multi-layer perceptrons, (MLPs), Radial basis function networks (RBFs) and Generalised regression neural networks. Our results suggest considerable improvements in accuracy can be achieved by the use of simple network sensitivity analysis, compared to genetic algorithms, and both forms of principal component analysis.

@InProceedings{AWA03, 
 	 author =  {Addison, J. F. Dale and Wermter, Stefan and Arevian, Garen},  
 	 title = {A Comparison of Feature Extraction and Selection Techniques}, 
 	 booktitle = {Proceedings of the International Conference on Artificial Neural Networks},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {212--215},
 	 year = {2003},
 	 month = {Jun},
 	 publisher = {Springer},
 	 doi = {}, 
 }