Abstract
Several hybrid gene selection algorithms for cancer classification that employ bio-inspired evolutionary wrapper algorithm have been proposed in the literature and show good classification accuracy. In our recent previous work, we proposed a new wrapper gene selection method based-on firefly algorithm named FF-SVM. In this work, we will improve the classification performance of FF-SVM algorithm by proposed a new hybrid gene selection algorithm. Our new biomarker gene discovery algorithm for microarray cancer gene expression analysis that integrates f-score filter method with Firefly feature selection method alongside with SVM classifier named FFF-SVM is proposed. The classification accuracy for the selected gene subset is measured by support vector machine SVM classifier with leave-one-out cross validation LOOCV. The evaluation of the FFF-SVM algorithm done by using five benchmark microarray datasets of binary and multi class. To show result validation of the proposed we compare it with other related state-of-the-art algorithms. The experiment proves that the FFF-SVM outperform other hybrid algorithm in terms of high classification accuracy and low number of selected genes. In addition, we compare the proposed algorithm with previously proposed wrapper-based gene selection algorithm FF-SVM. The result show that the hybrid-based algorithm shoe higher performance than wrapper based. The proposed algorithm is an improvement of our previous proposed algorithm.
Highlights
Gene expression profile provides valuable results that contribute to solving microarray data analysis problems
Experimental analysis and result demonstrate that the proposed algorithm FF-SVM provides high classification accuracy with small number of informative and predictive genes than other wrapper-based approaches proposed in the stat-ofart
We developed a new hybrid gene selection algorithm that combines f-score and Firefly feature selection method with SVM classification approach named FFF-SVM to select the most informative and predictive genes that cause cancer using Microarray gene expression profile
Summary
Gene expression profile provides valuable results that contribute to solving microarray data analysis problems. M. Alshamlan: New Bio-Marker Gene Discovery Algorithms for Cancer Gene Expression Profile the Firefly technique has not been apply as a wrapper method or hybrid method as a biomarker gene selection for microarray data classification. In [23] we proposed a new wrapper gene selection method for microarray gene expression profile cancer classification that employ Firefly algorithm alongside with SVM classifier named FF-SVM. Experimental analysis and result demonstrate that the proposed algorithm FF-SVM provides high classification accuracy with small number of informative and predictive genes than other wrapper-based approaches proposed in the stat-ofart. We developed a new hybrid gene selection algorithm that combines f-score and Firefly feature selection method with SVM classification approach named FFF-SVM to select the most informative and predictive genes that cause cancer using Microarray gene expression profile.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have