Asian Journal of Information Technology

Year: 2016
Volume: 15
Issue: 22
Page No. 4438 - 4444

Rough Set Based Approach for Multiclass Breast Tissue Classification

Authors : V.P. Sumathi, K. Kousalya and V. Vanitha

References

Daliri, M.R., 2015. Combining extreme learning machines using support vector machines for breast tissue classification. Comput. Methods Biomech. Biomed. Eng., 18: 185-191.
Direct Link  |  

Grzymala, B.J.W., 1992. LERS-A System For Learning From Examples Based on Rough Sets. In: Intelligent Decision Support. Roman, S. (Ed.). Springer Netherlands, Berlin, Germany, ISBN: 978-90-481-4194-4, pp: 3-18.

Janusz, A. and S. Stawicki, 2011. Applications of Approximate Reducts to the Feature Selection Problem. In: Rough Sets and Knowledge Technology. JingTao, Y., S. Ramanna, G. Wang and Z. Suraj (Eds.). Springer Berlin Heidelberg, Berlin, Germany, ISBN: 978-3-642-24424-7, pp: 45-50.

Jossinet, J., 1996. Variability of impedivity in normal and pathological breast tissue. Med. Biol. Eng. Comput., 34: 346-350.
CrossRef  |  Direct Link  |  

Liasis, G., C. Pattichis and S. Petroudi, 2012. Combination of different texture features for mammographic breast density classification. Proceedings of the 2012 IEEE 12th International Conference on Bioinformatics and Bioengineering (BIBE), November 11-13, 2012, IEEE, Nicosia, Cyprus, ISBN: 978-1-4673-4357-2, pp: 732-737.

Lichman, M., 2013. UCI Machine Learning Repository. University of California, Irvine, California,.

Mustra, M., M. Grgic and K. Delac, 2012. Breast density classification using multiple feature selection. Automatika J. Control Measure. Electron. Comput. Commun., 53: 362-372.
CrossRef  |  Direct Link  |  

Nguyen, H.S., 2001. On efficient handling of continuous attributes in large data bases. Based Inf., 48: 61-81.
Direct Link  |  

Oliver, A., J. Freixenet, R. Marti, J. Pont, E. Perez, E.R.E. Denton and R. Zwiggelaar, 2008. A novel breast tissue density classification methodology. IEEE Trans. Inform. Technol. Biomed., 12: 55-65.
CrossRef  |  Direct Link  |  

Pawlak, Z. and A. Skowron, 2007. Rudiments of rough sets. Inform. Sci., 177: 3-27.
CrossRef  |  Direct Link  |  

Pawlak, Z., 1991. Rough Sets: Theoretical Aspects of Reasoning about Data. 1st Edn., Kluwer Academic Publishers, London, UK., ISBN-13: 9780792314721.

Riza, L.S., A. Janusz, C. Bergmeir, C. Cornelis and F. Herrera et al., 2014. Implementing algorithms of rough set theory and fuzzy rough set theory in the R package roughsets. Inf. Sci., 287: 68-89.
Direct Link  |  

Shen, Q. and A. Chouchoulas, 2000. A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems. Eng. Appl. Artif. Intell., 13: 263-278.

Sheshadri, H.S. and A. Kandaswamy, 2007. Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms. Comput. Med. Imaging Graphics, 31: 46-48.
Direct Link  |  

Silva, D.J.E., D.J.M. Sa and J. Jossinet, 2000. Classification of breast tissue by electrical impedance spectroscopy. Med. Biol. Eng. Comput., 38: 26-30.
CrossRef  |  Direct Link  |  

Stefanowski, J., 1998. On rough set based approaches to induction of decision rules. Rough Sets Knowl. Discovery, 1: 500-529.
Direct Link  |  

Vaidehi, K. and T.S. Subashini, 2015. Breast tissue characterization using combined K-NN classifier. Indian J. Sci. Technol., 8: 23-26.
CrossRef  |  Direct Link  |  

Virmani, J., N. Dey and V. Kumar, 2016. PCA-PNN and PCA-SVM Based CAD Systems For Breast Density Classification. In: Applications of Intelligent Optimization in Biology and Medicine. Hassanien, A.E., C. Gorsan and M.F. Tolba (Eds.). Springer International Publishing, Berlin, Germany, ISBN: 978-3-319-21211-1, pp: 159-180.

Wroblewski, J., 2001. Ensembles of classifiers based on approximate reducts. Based Inf., 47: 351-360.
Direct Link  |  

Design and power by Medwell Web Development Team. © Medwell Publishing 2024 All Rights Reserved