AUTHORS: Kieran Greer
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ABSTRACT: This paper extends the earlier work on an oscillating error correction technique. Specifically, it extends the design to include further corrections, by adding new layers to the classifier through a branching method. This technique is still consistent with earlier work and also neural networks in general. With this extended design, the classifier can now achieve the high levels of accuracy reported previously.
KEYWORDS: classifier, oscillating error, neural network, multiple layers, branching, cellular automata
REFERENCES:
[1] Akkar, H.A. and Mahdi, F.R., 2016. Evolutionary algorithms for neural networks binary and real data classification. Int J Sci Technol Res, 5(7), pp.55-60.
[2] Asim, A., Li, Y., Xie, Y. and Zhu, Y. (2002). Data Mining For Abalone, Computer Science 4TF3 Project, Supervised by Dr. Jiming Peng, Department of Computing and Software, McMaster University, Hamilton, Ontario.
[3] Dershowitz, N. and Falkovich, E. (2015). Cellular Automata are Generic, U. Dal Lago and R. Harmer (Eds.): Developments in Computational Models 2014 (DCM 2014). EPTCS 179, pp. 17-32, doi:10.4204/EPTCS. 179.2.
[4] Fisher, R.A. (1936). The use of multiple measurements in taxonomic problems, Annual Eugenics, 7, Part II, pp. 179-188, also in Contributions to Mathematical Statistics (John Wiley, NY, 1950).
[5] Forina, M. et al. (1991). PARVUS - An Extendible Package for Data Exploration, Classification and Correlation. Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno, 16147 Genoa, Italy.
[6] Frey, P.W. and Slate, D.J. (1991). Letter recognition using Holland-style adaptive classifiers, Machine learning, Vol. 6, No. 2, pp. 161-182.
[7] Friedman, J.H. (1989). Regularized Discriminant Analysis, Journal of the American Statistical Association, Vol. 84, No. 405, pp. 165-175.
[8] Greer, K. (2017). A New Oscillating-Error Technique for Classifiers, Cogent Engineering, Taylor and Francis Online, https://doi.org/10. 1080/23311916.2017.1293480. Also available on arXiv at https://arxiv.org/abs/ 1505.05312.
[9] Greer, K. (2015). A Single-Pass Classifier for Categorical Data, Special Issue on: IJCSysE Recent Advances in Evolutionary and Natural Computing Practice and Applications, Int. J. Computational Systems Engineering, Inderscience, Vol. 3, Nos. 1/2, pp. 27 - 34. Also available on arXiv at https://arxiv.org/abs/ 1503.02521.
[10] Greer, K. (2013). Artificial Neuron Modelling Based on Wave Shape, BRAIN. Broad Research in Artificial Intelligence and Neuroscience, Vol. 4, Issues 1-4, pp. 20-25, ISSN 2067-3957 (online), ISSN 2068-0473 (print).
[11] Gurevich, Y. (2000). Sequential Abstract State Machines Capture Sequential Algorithms, ACM Transactions on Computational Logic Vol. 1, No. 1, pp. 77 – 111, doi:10.1145/ 343369.343384. Available at https://research. microsoft.com/~gurevich/opera/141.pdf.
[12] Hayes-Roth, B. and Hayes-Roth, F. (1977). Concept Learning and the Recognition and Classification of Exemplars, Journal of Verbal Learning and Verbal Behavior, Vol. 16, No. 3, pp. 321-338.
[13] Kahraman, H.T., Sagiroglu, S. and Colak, I. (2013). The development of intuitive knowledge classifier and the modeling of domain dependent data, Knowledge-Based Systems, Vol. 37, pp. 283-295.
[14] Kurgan, L.A., Cios, K.J., Tadeusiewicz, R., Ogiela, M. and Goodenday, L.S. (2001). Knowledge Discovery Approach to Automated Cardiac SPECT Diagnosis, Artificial Intelligence in Medicine, Vol. 23, No. 2, pp 149-169.
[15] Liver dataset (2016). https://archive.ics.uci. edu/ml/datasets/Liver+Disorders.
[16] Lohweg, V., Dörksen, H., Hoffmann, J. L., Hildebrand, R., Gillich, E., Schaede, J., and Hofmann, J. (2013). Banknote authentication with mobile devices. In IS&T/SPIE Electronic Imaging (pp. 866507-866507). International Society for Optics and Photonics.
[17] Potter, M.A. and De Jong, K.A., 2000. Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary computation, Vol. 8, No. 1, pp.1- 29.
[18] UCI Machine Learning Repository (2016). https://archive.ics.uci.edu/ml/.
[19] Zoo database (2016). https://archive.ics.uci.edu/ml/datasets/Zoo.
