Abstract
One of the important problems in using gene expression profiles to forecast cancer is how to effectively select a few useful genes to build exact models from large amount of genes. Classification is also a major issue in data mining. The classification difficulties in medical area often classify medical dataset based on the outcomes of medical analysis or report of medical action by the medical practitioner. In this study, a prediction model is proposed for the classification of cancer based on gene expression profiles. Feature selection also plays a vital role in cancer classification. Feature selection techniques can be used to extract the marker genes to improve classification accuracy efficiently by removing the unwanted noisy and redundant genes. The proposed study discusses the bijective-soft-set-based classification method for gene expression data of three different cancers, which are breast cancer, lung cancer, and leukemia cancer. The proposed algorithm is also compared with fuzzy-soft-set-based classification algorithms, fuzzy KNN, and k-nearest neighbor approach. Comparative analysis of the proposed approach shows good accuracy over other methods.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
S. Udhaya Kumar, H. Hannah Inbarani, S. Senthil Kumar, “Bijective soft set based classification of Medical data”, International Conference on Pattern Recognition, Informatics and Medical Engineering, pp. 517–521, 2013.
Hamid Mahmoodian et al., “New Entropy-Based Method for Gene Selection”, IETE Journal of Research, vol. 55, no. 4, pp. 162–168, 2009.
K. Gong et al., “The Bijective soft set with its operations”, An International Journal on Computers & Mathematics with Applications, vol. 60, no. 8, pp. 2270–2278, 2010.
N. Kalaiselvi, H. Hannah Inbarani, “Fuzzy Soft Set Based Classification for Gene Expression Data”, International Journal of Scientific & Engineering Research, vol. 3, no. 10, 2012.
D. Molodtsov, “Soft set theory-first results”, An International Journal on Computers & Mathematics with Applications, vol. 37, no. 4–5, pp. 19–31, 1999.
Cheng-Jung Tsai, Chien-I Lee, Wei-Pang Yang, “A discretization algorithm based on Class-Attribute Contingency Coefficient”, An International Journal on Information Sciences, vol. 178, pp. 714–73, 2008.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this paper
Cite this paper
Udhaya Kumar, S., Hannah Inbarani, H., Senthil Kumar, S. (2014). Improved Bijective-Soft-Set-Based Classification for Gene Expression Data. In: Krishnan, G., Anitha, R., Lekshmi, R., Kumar, M., Bonato, A., Graña, M. (eds) Computational Intelligence, Cyber Security and Computational Models. Advances in Intelligent Systems and Computing, vol 246. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1680-3_14
Download citation
DOI: https://doi.org/10.1007/978-81-322-1680-3_14
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1679-7
Online ISBN: 978-81-322-1680-3
eBook Packages: EngineeringEngineering (R0)