Open AccessOpen Access


Identification of Bio-Markers for Cancer Classification Using Ensemble Approach and Genetic Algorithm

K. Poongodi1,*, A. Sabari2

1 Department of Computer Science and Engineering, K.S.Rangasamy College of Technology, Namakkal, Tamil Nadu, 637 215, India
2 Department of Information Technology, K.S.Rangasamy College of Technology, Namakkal, Tamil Nadu, 637 215, India

* Corresponding Author: K. Poongodi. Email:

Intelligent Automation & Soft Computing 2022, 33(2), 939-953.


The microarray gene expression data has a large number of genes with different expression levels. Analyzing and classifying datasets with entire gene space is quite difficult because there are only a few genes that are informative. The identification of bio-marker genes is significant because it improves the diagnosis of cancer disease and personalized medicine is suggested accordingly. Initially, the parallelized minimum redundancy and maximum relevance ensemble (mRMRe) is employed to select top m informative genes. The selected genes are then fed into the Genetic Algorithm (GA) that selects the optimal set of genes heuristically, which uses Mahalanobis Distance (MD) as the distance measure. This proposed method (mRMRe-GA) is applied to four microarray datasets using Support Vector Machine (SVM) as a classifier. The Leave One out Cross Validation (LOOCV) method is used to analyze the performance of the classifier. Comparative study of the proposed mRMRe-GA method is carried out with other methods. The proposed mRMRe-GA method significantly improves the classification accuracy with less number of selected genes.


Cite This Article

K. Poongodi and A. Sabari, "Identification of bio-markers for cancer classification using ensemble approach and genetic algorithm," Intelligent Automation & Soft Computing, vol. 33, no.2, pp. 939–953, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 812


  • 413


  • 0


Share Link

WeChat scan