Abstract
This paper is based on the optimization of linear weights in a radial basis function neural network that connects the hidden layer and the output layer. A new optimization algorithm called dualist algorithm is applied for choosing an optimal learning parameter. A conventional strategy of random selection of radial basis function (RBF) centers and the width, as well as the weights, are estimated by gradient descent method and least square methods, respectively. The ideology behind this study is to predict the occurrence of metastatic carcinoma in human cells by computational approaches. Our simulation consists of comparing the predictive accuracy of harmony search-radial basis function network (RBFN) and dualist-RBFN by optimizing the weight factor. The Wisconsin breast cancer dataset is used as a benchmark to experiment our training pattern. The learning rate (weight factor) is taken as the optimized parameter to obtain the best possible solution.
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References
Eccles, S.A., Welch, D.A.: Metastasis: recent discoveries and novel treatment strategies. Lancet 369(9574), 1742–1757 (2007)
Vellido, A., Biganzoli, E., Lisboa, P.J.G.: Machine learning in cancer research: implications for personalised medicine. In: ESANN, pp. 55–64 (2008)
Van Stiphout, R.G.P.M., Postma, E.O., Valentini, V., Lambin, P.: The contribution of machine learning to predicting cancer outcome. Artif. Intell. 350, 400 (2010)
Stojadinovic, A., Nissan, A., Eberhardt, J., Chua, T.C., Pelz, J.O., Esquivel, J.: Development of a Bayesian belief network model for personalized prognostic risk assessment in colon carcinomatosis. Am. Surg. 77(2), 221–230 (2011)
Exarchos, K.P., Goletsis, Y., Fotiadis, D.I.: Multiparametric decision support system for the prediction of oral cancer reoccurrence. IEEE Trans. Inf Technol. Biomed. 16(6), 1127–1134 (2012)
Ahmad, L.G., Eshlaghy, A.T., Poorebrahimi, A., Ebrahimi, M., Razavi, A.R.: Using three machine learning techniques for predicting breast cancer recurrence. J. Health Med. Inform. 4(124), 3 (2013)
Chen, Y., Sun, J., Huang, L.C., Xu, H., Zhao, Z.: Classification of cancer primary sites using machine learning and somatic mutations. BioMed Res. Int. 2015 (2015)
Wang, Z., Wen, X., Yaohong, L., Yao, Y., Zhao, H.: Exploiting machine learning for predicting skeletal-related events in cancer patients with bone metastases. Oncotarget 7(11), 12612 (2016)
Ali, A.-R.: Deep Learning in Oncology–Applications in Fighting Cancer. (2017)
Dokduang, K., Chiewchanwattana, S., Sunat, K., Tangvoraphonkchai, V.: A comparative machine learning algorithm to predict the bone metastasis cervical cancer with imbalance data problem. Recent Advances in Information and Communication Technology, pp. 93–102. Springer, Cham (2014)
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Vijayeeta, P., Das, M.N., Mishra, B.S.P. (2020). An Efficient Approach to Optimize the Learning Rate of Radial Basis Function Neural Network for Prediction of Metastatic Carcinoma. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_80
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DOI: https://doi.org/10.1007/978-981-13-9042-5_80
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