Efficient Feature Selection Model for Gene Expression Data

Abstract:

Finding subset of informative gene is very crucial for biology process because several genes increase sharply and most of them are not related with others. In general, feature selection technique consists of two steps 1) all genes is ranked by a filter approach 2) rank list is sent to a wrapper approach. Nevertheless, the accuracy rate for recognition gene is not enough. Therefore, this paper proposes efficient feature selection model for gene expression data. First, two filter approaches are used to define many subset of attribute such as Correlation based Feature Selection (Cfs) and Gain Ratio (GR). Second, wrapper approach is used to evaluate each length of attribute that based on Support Vector Machine (SVM) and Random Forest (RF). The result of experiment depicts CfsSVM, CfsRF, GRSVM, and GRRF based on proposed model produce higher accuracy rate such as 87.10%, 90.32%, 87.10, and 88.71%, respectively.

• Periodical: Applied Mechanics and Materials (Volumes 110-116)
• Pages: 1948-1952
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.110-116.1948
• Online since: October 2011
• Authors: Patharawut Saengsiri, Sageemas Na Wichian, Phayung Meesad
• Keywords: Feature Selection, Gene Expression Data, Random Forest (RF), Support Vector Machine (SVM)

References

[1] P. Lance, H. Ehtesham, and L. Huan, Subspace Clustering for High Dimensional Data: A Review, SIGKDD Explor. Newsl. 1931-0145, Vol. 6 (2004), pp.90-105.

DOI: 10.1145/1007730.1007731

[2] Mukherjee, S. and S. J. Roberts. A Theoretical Analysis of Gene Selection, Computational Systems Bioinformatics Conference, CSB 2004. Proceedings (2004), pp.131-141.

DOI: 10.1109/csb.2004.1332425

[3] P. Saengsiri, S.N. Wichian, P. Meesad, and U. Herwig, Comparison of hybrid feature selection models on gene expression data, in 8th International Conference on ICT and Knowledge Engineering (2010), pp.13-18.

DOI: 10.1109/ictke.2010.5692905

[4] Pádraig Cunningham, Dimension Reduction, Technical Report UCD-CSI-2007-7, August , (2007), pp.1-4.

[5] Jaeger J., R. Sengupta , W. L. Ruzzo, Improved Gene feature selection for Classification of Microarrays, Pacific Symposium on Biocomputing 8 (2003), pp.53-64.

DOI: 10.1142/9789812776303_0006

[6] Cheng-San, Y., C. Li-Yeh, et al, A Hybrid Approach for Selecting Gene Subsets Using Gene Expression Data, " Soft Computing in Industrial Applications, SMCia , 08. IEEE Conference (2008), pp.159-164.

DOI: 10.1109/smcia.2008.5045953

[7] Hikaru Mitsubayashi, Seiichiro Aso, Tomomasa Nagashima, and Yoshifumi Okada, Accurate and Robust Gene feature selectionfor Disease Classification Using a Simple Statistic, ΙSSN 0973-2063 (online) 0973-2063 (print), Bioinformation 3(2) (2008).

DOI: 10.6026/97320630003068

[8] Jin-Hyuk H. and C. Sung-Bae, Cancer classification incremental gene feature selectionbased on DNA microarray data, Computational Intelligence in Bioinformatics and Computational Biology, IEEE Symposium (2008), pp.70-74.

DOI: 10.1109/cibcb.2008.4675761

[9] Kamal A., X. Zhu, A. Pandya, S. Hsu, and M. hoaib, The Impact of Gene feature selectionon Imbalanced Microarray Expression Data, Bioinformatics and Computational Biology (2009), pp.259-269.

DOI: 10.1007/978-3-642-00727-9_25

[10] Mark A. Hall, Correlation-based Feature Selection for Machine Learning, Doctor of Philosphy Department of Computer Science, The University of Waikato Newzealand (1999).

[11] R. Gray, Entropy and Information Theory, Springer (1990), pp.12-18.