Multiclass lung cancer diagnosis by gene expression programming and microarray datasets

There are various types of lung cancer and they can be differentiated
by the cell size as well as the growth pattern. They are all treated differently. Classification of the various types of lung cancer assists in determining the specified treatments to decrease the fatality rates. In this paper, we broaden the analysis of lung by using gene expression data, binary decomposition strategies and Gene Expression Programming (GEP) technique, aiming at achieving better classification performance. Classification performance was assessed and compared between our GEP models and three representative machine learning techniques, SVM, NNW and C4.5 on real microarray Lung tumor datasets. Dependability was evaluated by the cross-informational collection validation. The evaluation results demonstrate that our technique can achieve better classification performance in terms of Accuracy, standard deviation and range under the recipient working trademark bend. The proposed technique in this paper provides a helpful tool for Lung cancer classification.