Abstract
The functional link neural network (FLNN) increases the input dimension by functionally expanding the input features. In this paper, modifications to the FLNN are proposed for undertaking data classification tasks. The main objective is to optimize the FLNN by formulating a parsimonious network with less complexity and lower computational burden as compared with the original FLNN. The methodology consists of selecting a number of important expanded features to build the FLNN structure. It is based on the rationale that not all the expanded features are equally important in distinguishing different target classes. As such, we modify the FLNN in a way that less—relevant and redundant expanded input features are identified and discarded. In addition, instead of using the back-propagation learning algorithm, adjustment of the network weights is formulated as an optimisation task. Specifically, the genetic algorithm is used for both feature selection as well as weight tuning in the FLNN. An experimental study using benchmark problems is conducted to evaluate the efficacy of the modified FLNN. The empirical results indicate that even though the structure of the modified FLNN is simpler, it is able to achieve comparable classification results as those from the original FLNN with fully expanded input features.
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Dash, P., Satpathy, H., Liew, A., Rahman, S.: A real-time short-term load forecasting system using functional link network. IEEE Trans. Power Syst. 12(2), 675–680 (1997)
Tsai, C.F., Wu, J.W.: Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Syst. Appl. 34(4), 2639–2649 (2008)
Shahzadeh, A., Khosravi, A., Nahavandi, S.: Improving load forecast accuracy by clustering consumers using smart meter data. In: International Joint Conference on Neural Networks (IJCNN), Killarney, pp. 1–7, July 2015
Miller, A., Blott, B., et al.: Review of neural network applications in medical imaging and signal processing. Med. Biol. Eng. Comput. 30(5), 449–464 (1992)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press (1995)
Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press (2007)
Lippmann, R.P.: Pattern classification using neural networks. IEEE Commun. Mag. 27(11), 47–50 (1989)
Cochocki, A., Unbehauen, R.: Neural Networks for Optimization and Signal Processing. Wiley (1993)
Khan, J., Wei, J.S., Ringner, M., Saal, L.H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C.R., Peterson, C., et al.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med. 7(6), 673–679 (2001)
Meireles, M.R., Almeida, P.E., Simões, M.G.: A comprehensive review for industrial applicability of artificial neural networks. IEEE Trans. Ind. Electron. 50(3), 585–601 (2003)
Kaastra, I., Boyd, M.: Designing a neural network for forecasting financial and economic time series. Neurocomputing 10(3), 215–236 (1996)
Pao, Y.H., Takefuji, Y.: Functional-link net computing: theory, system architecture, and functionalities. IEEE Comput. J. 25(5), 76–79 (1992)
Pao, Y.-H., Park, G.-H., Sobajic, D.J.: Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6(2), 163–180 (1994)
Igelnik, B., Pao, Y.-H.: Additional perspectives on feedforward neural-nets and the functional-link. In: Proceedings of the 1993 International Joint Conference on Neural Networks (IJCNN93), Nagoya, pp. 2284–2287 (1993)
Bebarta, D.K., Rout, A.K., Biswal, B., Dash, P.K.: Forecasting and classification of Indian stocks using different polynomial functional link artificial neural networks. In: Proceedings of the Annual IEEE India Conference (INDICON), Kochi, pp. 178–182 (2012)
Nanda, S.K., Tripathy, D.P.: Application of functional link artificial neural network for prediction of machinery noise in opencast mines. Adv. Fuzzy Syst. 2011, 4 (2011)
Dehuri, S., Roy, R., Cho, S.-B., Ghosh, A.: An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification. J. Syst. Softw. 85(6), 1333–1345 (2012)
Mall, S., Chakraverty, S.: Hermite functional link neural network for solving the Van der Pol Duffing oscillator equation. Neural Comput. 28(8), 1574–1598 (2016)
Mall, S., Chakraverty, S.: Numerical solution of nonlinear singular initial value problems of Emden-Fowler type using Chebyshev neural network method. Neurocomputing 149(Part B), 975–982 (2015)
Hassim, Y., Ghazali, R.: Optimizing functional link neural network learning using modified bee colony on multi-class classifications. In: Advances in Computer Science and its Applications, vol. 279, Sec. 23, pp. 153–159. Springer, Berlin (2014)
Naik, B., Nayak, J., Behera, H., Abraham, A.: A harmony search based gradient descent learning-FLANN (HS-GDL-FLANN) for classification. In: Computational Intelligence in Data Mining, vol. 2, pp. 525–539, Springer (2015)
Bebarta, D., Venkatesh, G.: A low complexity FLANN architecture for forecasting stock time series data training with meta-heuristic firefly. In: Proceedings of the International Conference on Computational Intelligence in Data Mining, Odisha, pp. 377–385 (2016)
Dehuri, S., Cho, S.-B.: A comprehensive survey on functional link neural networks and an adaptive PSO-BP learning for CFLNN. Neural Comput. Appl. 19(2), 187–205 (2010)
Dehuri, S., Cho, S.-B.: A hybrid genetic based functional link artificial neural network with a statistical comparison of classifiers over multiple datasets. Neural Comput. Appl. 19(2), 317–328 (2010)
Scardapane, S., Wang, D., Panella, M., Uncini, A.: Distributed learning for random vector functional-link networks. Inf. Sci. 301, 271–284 (2015)
Dash, C.S.K., Dehuri, S., Cho, S.-B., Wang, G.-N.: Towards crafting a smooth and accurate functional link artificial neural networks based on differential evolution and feature selection for noisy database. Int. J. Comput. Intell. Syst. 8(3), 539–552 (2015)
Dehuri, S., Mishra, B.B., Cho, S.-B.: Genetic feature selection for optimal functional link artificial neural network in classification. In: Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), Daejeon, pp. 156–163 (2008)
Sierra, A., Macias, J., Corbacho, F.: Evolution of functional link networks. IEEE Trans. Evol. Comput. 5(1), 54–65 (2001)
Dehuri, S., Cho, S.-B.: Evolutionarily optimized features in functional link neural network for classification. Expert Syst. Appl. 37(6), 4379–4391 (2010)
Mili, F., Hamdi, M.: A comparative study of expansion functions for evolutionary hybrid functional link artificial neural networks for data mining and classification. In: International Conference on Computer Applications Technology (ICCAT), Sousse, pp. 1–8 (2013)
Liu, L., Manry, M., Amar, F., Dawson, M., Fung, A.: Image classification in remote sensing using functional link neural networks. In: Proceedings of the IEEE Symposium on Image Analysis and Interpretation, Southwest, pp. 54–58 (1994)
Jang, J.-S.R., Sun, C.-T., Mizutani, E.: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, New Jersey (1997)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman, Boston, MA (1989)
Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms, 1st edn. Springer Science & Business Media, Dordrecht (2002)
Chalmers, D.J.: The evolution of learning: an experiment in genetic connectionism. In: Proceedings of the Connectionist Models Summer School, San Mateo, CA (1990)
Leung, F.H.F., Lam, H.K., Ling, S.H., Tam, P.K.S.: Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans. Neural Netw. 14, 79–88 (2003)
Kaya, M., Alhajj, R.: Genetic algorithm based framework for mining fuzzy association rules. Fuzzy Sets Syst. 152, 587–601 (2005)
Chen, C.L.P., LeClair, S.R., Pao, Y.-H.: An incremental adaptive implementation of functional-link processing for function approximation, time-series prediction, and system identification. Neurocomputing 18, 11–31 (1998)
Lichman, M.: UCI Machine Learning Repository (2013). http://archive.ics.uci.edu/ml/
Prechelt, L.: Proben1 a set of neural network benchmark problems and benchmarking rules. Technical Report 21/94, Fakultt fr Informatik, Univ. Karlsruhe, Karlsruhe, Germany, Sept 1994
Yao, X., Liu, Y.: A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Netw. 8(3), 694–713 (1997)
Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning, Neural and Statistical Classification. Ellis Horwood (1994)
Ster, B., Dobnikar, A.: Neural networks in medical diagnosis: comparison with other methods. In: Proceedings of the International Conference EANN, pp. 427–430 (1996)
Thimm, G.: Optimization of high order perceptrons. Ph.D. dissertation, 1633, cole Polytechnique Fdrale de Lausanne, Lausanne, Switzerland, June 1997
Setiono, R., Hui, L.C.K.: Use of a quasinewton method in a feedforward neural-network construction algorithm. IEEE Trans. Neural Netw. 6, 273–277 (1995)
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Babaei, T., Lim, C.P., Abdi, H., Nahavandi, S. (2017). A Modified Functional Link Neural Network for Data Classification. In: Bhatti, A., Lee, K., Garmestani, H., Lim, C. (eds) Emerging Trends in Neuro Engineering and Neural Computation. Series in BioEngineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-3957-7_13
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