Heterogeneous versus Homogeneous Machine Learning Ensembles

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The research demonstrates efficiency of the heterogeneous model ensemble application for a cancer diagnostic procedure. Machine learning methods used for the ensemble model training are neural networks, random forest, support vector machine and offspring selection genetic algorithm. Training of models and the ensemble design is performed by means of HeuristicLab software. The data used in the research have been provided by the General Hospital of Linz, Austria.

[1] Hennicker, R., Klarl, A., “Foundations for Ensemble Modelling – The Helena Approach,” Lecture Notes in Computer Science: Specification, Algebra, and Software, 2014, vol. 8373, pp. 359–381.

[2] Zhou, Z.-H. Ensemble Methods: Foundations and algorithms. Boca Raton: Chapman and Hall/CRC, 2012. 236 p.

[3] Re, M., Valentini, G. Advances in Machine Learning and Data Mining for Astronomy. Boca Raton: Chapman & Hall/CRC, 2012, 744 p.

[4] Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. M. Affenzeller, S. Wagner, S. Winkler. Boca Raton: Chapman & Hall/CRC, 2009. 379 p.

[5] Affenzeller, M., Wagner, S. “Offspring Selection: A New Self-Adaptive Selection Scheme for Genetic Algorithms,” in Proc. of the Int. Conf. in Coimbra, Portugal, 2005, pp. 218–221. http://dx.doi.org/10.1007/3-211-27389-1_52

[6] Cristianini, N., Shawe-Taylor, J. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge: Cambridge University Press, 2000. 198 p. http://dx.doi.org/10.1017/CBO9780511801389

[7] Burges, C.J.C. “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, 1998, vol. 2, pp. 121–167. http://dx.doi.org/10.1023/A:1009715923555

[8] SVM – Support Vector Machines [Online]. Accessed 12th November 2015. Available: https://www.dtreg.com/solution/view/20

[9] Auria, L., Moro, R.A. “Support Vector Machines as a Technique for Solvency Analysis,” Discussion papers of German Institute for Economic Research, 2008, 18 p.

[10] Cheung, V., Cannons, K. An Introduction to Neural Networks [Online]. Available: http://www2.econ.iastate.edu/tesfatsi/neuralnetworks.cheungcannonnotes.pdf. Accessed on: Nov. 10, 2015.

[11] Rogers, C. The Advantages of Artificial Neural Networks [Online]. Available: http://www.ehow.com/info_8148024_advantages-artificial-neural-networks.html. Accessed on: Nov. 10, 2015.

[12] Steinberg, D., Golovnya, M., Cardell, N.S. A Brief Overview to Random Forests [Online]. Available: http://nymetro.chapter.informs.org/prac_cor_pubs/RandomForest_SteinbergD.pdf. Accessed on: Nov. 10, 2015.

[13] A.L. Boulesteix, S. Janitza, J. Kruppa. “Overview of Random Forest Methodology and Practical Guidance with Emphasis on Computational Biology and Bioinformatics,” WIREs Data Mining &KnowledgeDiscovery, vol. 129, 2012, pp. 1–31.

[14] Breast cancer description [Online]. Available: http://www.cancer.gov/cancertopics/types/breast. Accessed on: Nov. 10, 2015.

[15] Cancer Statistics [Online]. Available: http://www.cancer.gov/statistics/. Accessed on: Nov. 10, 2015.

[16] Description of Melanoma [Online]. Available: http://www.skincancer.org/skin-cancer-information/melanoma. Accessed on: Nov. 10, 2015.

[17] Sarmady, S. “An Investigation on Genetic Algorithm Parameters, SiamakSarmady,” School of Computer Science, Universiti Sains Malaysia. 2007, 10 p.

[18] Petrakova, A. Uz mašīnapmācības metodēm balstīta heterogēnu modeļu ansambļa izveide. Master thesis. Riga Technical University, Riga, 2014, 130 p.

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