1 October 2011 Genetic algorithm for feature selection of MR brain images using wavelet co-occurence
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Proceedings Volume 8285, International Conference on Graphic and Image Processing (ICGIP 2011); 828557 (2011) https://doi.org/10.1117/12.913381
Event: 2011 International Conference on Graphic and Image Processing, 2011, Cairo, Egypt
Abstract
The selection of features has a considerable impact on the success or failure of classification process. Feature selection refers to the procedure of selecting a subset of informative attributes to build models describing data. The main purpose of feature selection is to reduce the number of features used in classification while maintaining high classification accuracy. A large number of algorithms have been proposed for feature subset selection. Here we compare classical sequential methods with the genetic approach in terms of the number of features, classification accuracy and reduction rate. Genetic Algorithm (GA) achieves an acceptable classification accuracy with only five of the available 44 features. The optimal feature such as mean of contrast, mean of homogeneity, mean of sum average, mean of sum variance and range of autocorrelation provide best classification performance. Similar classification performance is obtained with SFFS and SFBS but with larger feature set.
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Ahmed Kharrat, Ahmed Kharrat, Nacéra Benamrane, Nacéra Benamrane, Mohamed Ben Messaoud, Mohamed Ben Messaoud, Mohamed Abid, Mohamed Abid, } "Genetic algorithm for feature selection of MR brain images using wavelet co-occurence", Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 828557 (1 October 2011); doi: 10.1117/12.913381; https://doi.org/10.1117/12.913381
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