International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

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DOI Prefix: 10.47001/IRJIET

Investigating Sensitivity of Nonlinear Classifiers by Reducing Mean Square Error (MSE)

Abeer A. Al-MohdarAssistant Professor, Dept. of Information Technology, Faculty of Computers & Information, Hadhramout University, YemenAhmed A. BahashwanFaculty of Engineering & Petroleum, Dept. of Electronics & Communication Engineering, Hadhramout University, Yemen

Vol 4 No 8 (2020): Volume 4, Issue 8, August 2020 | Pages: 31-36

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 26-08-2020

doi Logo doi.org/10.47001/IRJIET/2020.408006

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Abstract

In this paper a new method is presented for handling the problems of Artificial Neural Networks (ANNs) in a Self-Organizing Map (SOM), the problems are the presence of noise and missing in datasets. Based on a robust and speedup of the investigation of a nonlinear classifications in SOM and Adaptive Linear Neuron ANNs (Adaline) algorithms that dealing with missing or noise data a new model proposed. the main objective of the proposed model is to contribute the advantages of all methods (SOM with Adaline) in rising the quality of SOM to handle the noise and missing value to improve its sensitivity of our model using combing the advantages of all methods (SOM with Adaline) in anew algorithm abbreviation (ADA-SOM). Simulation results show that the algorithm ADA-SOM achieved better performance and higher sensitivity to accurate MSE (Mean Square Error) than other standard classifiers, our model gets accurate benchmark results.

Keywords

Self-organizing-map, Adaptive Linear Neuron, missing data, Artificial Neural Networks, Unsupervised classifiers


Citation of this Article

Abeer A. Al-Mohdar, Ahmed A. Bahashwan, “Investigating Sensitivity of Nonlinear Classifiers by Reducing Mean Square Error (MSE)” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 4, Issue 8, pp 31-36, August 2020. https://doi.org/10.47001/IRJIET/2020.408006

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. T. Kohonen, “Self-Organizing Maps,” Springer-Verlag, Berlin, 1995.
  2. A. Sorjamaa, B. Maillet, P.  Merlin andA. Lendasse,“Some of for finding missing values,” European Symposium on Artificial Neural Networks, Bruges, Belgium, pp. 25-27, ISBN 2-930307-07-2, 2007.
  3. T.Samad, and S. Harp, “Self-organization with partial data. Network,” vol. 3, no. 2, pp. 205–212, 1992.
  4. A. Vellido,K. Gibert, C. Angulo, J.D. Martín Guerrero, “Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization,” Advances in Intelligent Systems and Computing, vol. 342, no. 976, DOI:10.1007/978-3-030-19642-4, 2020.
  5. Sun Y, BragaNeto U,ER. Dougherty, “Impact of missing value imputation on classification for dna microarray gene expression data: A model-based study EURASIP,” J Bioinformatics Syst Biol, vol. 44, no. 1, 2009.
  6. NJ.Horton and KP. Kleinman, “Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models,” The American Statistician vol. 61, pp. 79-90, 2007.
  7. R. Saranya1, K. Kuppusamy, “Performance analysis of ADALINE and MADALINE Network in classification of Thyroid Disease,” International Journal of Advanced Research Trends in Engineering and Technology (IJARTET), vol. 3, Special Issue 20, (April) 2016.
  8. JA.Saunders, N. Morrow-Howell,E. Spitznagel, P. Dor, and Proctor EK, et al., “Imputing missing data: A comparison of methods for social work researchers,” Social Work Research, vol. 30, no. 22, pp. 19-31, 2006.
  9. AWC.Liew,NF. Law,H. Yan, “Missing value imputation for gene expression data: computational techniques to recover missing data from available information,”  Briefings in Bioinformatics, vol. 12, pp. 498-513, 2011.
  10. A. deBrevern,S. Hazout,A. Malpertuy, “Influence of microarrays experiments missing values on the stability of gene groups by hierarchical clustering,”  BMC Bioinformatics, vol. 5, pp. 1-12, 2004.
  11. D.Wang,Y. Lv,Z. Guo,X. Li,Y. Li, et al., “Effects of replacing the unreliable cdna microarray measurements on the disease classification based on gene expression profiles and functional modules,” Bioinformatics, vol. 22, pp. 2883-2889, 2006.
  12. HD. Lewis, “Missing data in clinical trials. New England Journal of Lewis HD Missing data in clinical trials,” New England Journal of Medicine, vol. 367, pp. 2557-2558, 2012.
  13. Little RJA, Rubin DB, “Statistical Analysis with Missing Data, Wiley-Interscience, 2002.
  14. DB. Rubin, “Multiple Imputation for Non-response in Survey,” John Wiley and Sons, 1987.
  15. JG.Ibrahim, MH. Chen, SR. Lipsitz, AH. Herring,“Missing-data methods for generalized linear models,” Journal of the American Statistical Association, vol. 100, pp. 332-346, 2005.
  16. T. JOUR, A. Longlong, A. Kenli, A.Canqun, and A. Qi, “A multiple kernel density clustering algorithm for incomplete datasets in bioinformatics,” vol. 12, DOI- 10.1186/s12918-018-0630-6, BMC Systems Biology, 2018.
  17. C. Ryan, D. Greene andG.Cagney, et al., “Missing value imputation for epistatic MAPs,” BMC Bioinformatics 11, 197. https://doi.org/10.1186/1471-2105-11-197, 2010.
  18. B. Yang, F. Liu, C. Ren, Z. Ouyang, Z. Xie,X. Bo, and W. Shu,  BiRen, “predicting enhancers with a deep-learning-based model using the DNA sequence alone,” Bioinformatics, vol. 33, no. 13, doi.org/10.1093/bioinformatics/btx105, 2017.
  19. S.Ouadfeul, andL. Aliouane, Lithofacies, “Classification Using the Multilayer Perceptron and the Self-organizing Neural Networks,” Neural Information Processing, vol. 7667, ISBN: 978-3-642-34499-2, 2012.
  20. J. L. Bernier, J. Ortega and E. Ros, “A Quantitative Study of Fault Tolerance, Noise Immunity, and Generalization Ability of MLPs,” Neural Computation, vol. 12, no. 12, pp. 2941-2964, 2006.
  21. Ji.Sai, Ping Yang, Shuiming Zhong1, Jin Wang and Jeong-Uk Kim, “ A Novel Adaptive Architecture Pruning Algorithm for Madalines,” International Journal of Hybrid Information Technology, International Journal of Hybrid Information Technology vol. 9, no. 6, pp. 303-316 http://dx.doi.org/10.14257/ijhit.2016.9.6.27.
  22. M. Pampaka, G. Hutcheson, and J. Williams, “Handling missing data: analysis of a challenging data set using multiple imputation,” Int. J. Res. Method Educ., vol. 39, no. 1, pp. 19–37, 2016.
  23. https://www.kaggle.com/joelpires/hepatitis-c-virus-for-egyptian-patients-data-set.
  24. https://github.com/nikicc/orange/tree/master/Orange/datasets

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