Classifier Design with Feature Selection for Heart Disease Classification
Heart Disease and Stroke Statistics - at a glance http://www.heart.org
Bing Xue, ”Particle Swarm Optimisation for Feature Selection in Classification”, A Ph. D. thesis submitted to the Victoria University of Wellington, 2014.
Bing Xue, Mengjie Zhang, Will N. Browne, Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach, IEEE Transactions On Cybernetics, Vol. 43, No. 6, December 2013.
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182 – 197, Apr. 2002.
Waikato Environment for Knowledge Analysis (WEKA) by the Machine Learning Group at the University at Waikato. Freely available at www.cs.waikato.ac.nz/ml/weka
T. Abeel, Y. V. de Peer, and Y. Saeys, Java-ml: A machine learning library,J. Mach. Learn. Res., vol. 10, pp. 931934, Dec. 2009.
Binh Tran, Bing Xue, and Mengjie Zhang, ”Overview of Particle Swarm Optimisation for Feature Selection in Classification”, SEAL 2014, LNCS 8886, pp. 605-617, 2014.
K.Rajeswari, V.Vaithiyanathan, S. V.Pede, ”Feature Selection for Classification in Medical Data Mining”,IJETTCS ,Volume 2, Issue 2, March April 2013.
B. Xue, L. Cervante, L. Shang, W. N. Browne, M. Zhang,” Multi-Objective Evolutionary Algorithms For Filter Based Feature Selection In Classification” ,International Journal on Artificial Intelligence Tools, Vol. 22, No. 4 (31 pages), 2013.
A. Khan, A.R. Baig, ”Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm”, Journal of Applied Research and Technology (JART), Vol. 13. Num. 01. February 2015.
Barnali Sahu , Debahuti Mishra, ”A Novel Feature Selection Algorithm using Particle Swarm Optimization for Cancer Microarray Data”, International Conference on Modeling Optimization and Computing (ICMOC-2012), Procedia Engineering 38 , pp. 27 - 31, 2012.
P. J. Rauss, J. M. Daida, and S. Chaudhary, Classification of spectral imagery using genetic programming, in Proc. Genetic Evolutionary Com-putation Conf., pp. 733726, 2000.
D. Agnelli, A. Bollini, and L. Lombardi, Image classification: an evolutionary approach, Pattern Recognit. Lett., vol. 23, pp. 303309, 2002.
S. A. Stanhope and J. M. Daida, Genetic programming for automatic target classification and recognition in synthetic aperture radar imagery, in Evolutionary Programming VII, Proc. 7th Annu. Conf. Evolutionary Programming, pp. 735-744, 1998.
I. De Falco, A. Della Cioppa, and E. Tarantino, Discovering interest-ing classification rules with genetic programming, Appl. Soft Comput., vol.23, pp. 113, 2002.
G. Dounias, A. Tsakonas, J. Jantzen, H. Axer, B. Bjerregaard, and D.Keyserlingk, Genetic programming for the generation of crisp and fuzzy rule bases in classification and diagnosis of medical data, in Proc. 1st Int. NAISO Congr. Neuro Fuzzy Technologies,Academic Press, Canada, 2002.
Asha Gowda Karegowda , A.S. Manjunath , M.A. Jayaram ”Application Of Genetic Algorithm Optimized Neural Network Connection Weights For Medical Diagnosis Of Pima Indians Diabetes”, International Journal on Soft Computing ( IJSC ), Vol.2, No.2, pp. 15-23, May 2011.
K. Polat, S. Gunes, A. Aslan, ”A cascade learning system forclassifica-tion of diabetes disease: Generalized discriminant analysis andleast square support vector machine”, Expert systems with applications, vol.34(1), pp. 214-221, 2008.
Filipe de L. Arcanjo, Gisele L. Pappa, Paulo V. Bicalho, Wagner Meira Jr., Altigran S. da Silva, Semi-supervised Genetic Programming for Classification, GECCO11, July 1216, 2011, Dublin, Ireland.
Kennedy J., Eberhart R. ”Particle Swarm Optimization”, Proceedings of IEEE International Conference on Neural Networks, pp.1942-1948. 1995.
J. R. Koza,Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press, 1992.
UCI Machine Learning Repository, Available online at http://www.ics.uci.edu/ml/datasets.htm
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