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Regression-Based Approach For Feature Selection In Classification Issues. Application To Breast Cancer Detection And Recurrence


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1. Turban, E., Aronson, J.E., Liang, T.-P., Decision Support Systems and Intelligent Systems, 7th edition, Prentice-Hall of India, (2006).Search in Google Scholar

2. Andersson, B., Andersson, R., Ohlsson, M., Nilsson, J., Prediction of Severe Acute Pancreatitis at Admission to Hospital Using Artificial Neural Networks, Pancreatology. Vol. 11, pp. 328-335, (2011).10.1159/00032790321757970Search in Google Scholar

3. Park, Y., Luo, L., Parhi, K.K., Netoff, T., Seizure prediction with spectral power of EEG using cost-sensitive support vector machines, Epilepsia, Vol. 52, pp. 1761-1770, (2011).10.1111/j.1528-1167.2011.03138.x21692794Search in Google Scholar

4. Salas-Gonzalez, D., Gorriz, J.M., Ramírez, J, et al., Computer-aided diagnosis of Alzheimer's disease using support vector machines and classification trees, Phys Med Biol,. Vol. 55, No. 10, pp. 2807-2817, (2010).10.1088/0031-9155/55/10/00220413829Search in Google Scholar

5. Gorunescu, F., Gorunescu, M., Saftoiu, A., Vilmann, P., Belciug, S., Competitive/collaborative neural computing system for medical diagnosis in pancreatic cancer detection, Expert Systems, Vol. 28, No. 1, pp. 33-44, (2011).10.1111/j.1468-0394.2010.00540.xSearch in Google Scholar

6. Belciug, S., Gorunescu, F., A hybrid neural network/genetic algorithm applied to breast cancer detection and recurrence, Expert Systems, Vol. 30, No. 3, pp. 243-254, (2013).10.1111/j.1468-0394.2012.00635.xSearch in Google Scholar

7. Liu, H., Motoda, H., Computational methods of feature selection, Editors eds., Chapman and Hall/CRC, (2007).10.1201/9781584888796Search in Google Scholar

8. Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.A., Feature extraction. Foundations and applications, Editors eds., Springer, (2006).10.1007/978-3-540-35488-8Search in Google Scholar

9. Karabatak, M., Ince, M.C., An expert system for detection of breast cancer based on association rules and neural network, Expert Systems with Applications, Vol. 36, pp. 3465-3469, (2009).10.1016/j.eswa.2008.02.064Search in Google Scholar

10. Stoean, C., Stoean, R., Lupsor, M., Stefanescu, H., Badea, R., Feature selection for a cooperative coevolutionary classifier in liver fibrosis diagnosis, Computers in Biology and Medicine, Vol. 41, pp. 238-246, (2011).10.1016/j.compbiomed.2011.02.00621419402Search in Google Scholar

11. Shen, Q., Meia, Z., Ye, B-X., Simultaneous genes and training samples selection by modified particle swarm optimization for gene expression data classification, Computers in Biology and Medicine, Vol. 39, No. 7, pp. 646–649, (2009).10.1016/j.compbiomed.2009.04.00819481202Search in Google Scholar

12. Marinakis, Y., Dounias, G., Jantzen, J., Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification. Computers in Biology and Medicine, Vol. 39, No. 1, pp. 69–78, (2009).10.1016/j.compbiomed.2008.11.00619147127Search in Google Scholar

13. Gorunescu, F., Data Mining: Concepts, Models and Techniques, Springer-Verlag, Berlin Heidelberg, (2011).Search in Google Scholar