Biomarker Discovery based on Hybrid Firefly Optimization Algorithm and Hybrid Adaboost on ANNs Classifier
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Abstract
Biomarker discovery is one of the biggest challenges in cancer research. In this paper, a new approach based on FFF and adaptive boosting on ANN classifier is proposed for finding genes that can classify the group of cancer correctly. In this approach, FFF as firefly wrapper-based feature selection is used to perform gene selection and Adaboost on ANN classifier with 7 cross fold cross validation is adopted as the classifier. The proposed approach is tested on five benchmark microarray gene expression profiles namely, Colon, SBRCT, Leukemia 1, Leukemia 2 and Lung. The experimental results show that our proposed method can select the most informative gene subsets by reducing the dimension of the data set and improve classification accuracy when compared to the existing algorithm. Our proposed algorithm shows the best classification accuracy compared with the FFF-SVM.
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