Abstract
In this paper, we present a new evolutionary instance-based learning method by integrating the bacterial foraging optimization (BFO) technique with fuzzy k-nearest neighbor classifier (FKNN), termed as BFO-FKNN. In the proposed approach, the issue of parameter tuning problem in FKNN is tackled using the BFO technique. The effectiveness of the proposed method has been rigorously evaluated against the Parkinson’s disease (PD) diagnosis problem. The simulation results have shown that the proposed approach outperforms the other two counterparts via 10-fold cross validation analysis. In addition, compared to the existing methods in previous studies, the proposed method can also be regarded as a promising success with the excellent classification accuracy of 96.39%.
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References
Jóźwik, A.: A learning scheme for a fuzzy k-NN rule. Pattern Recognition Letters 1(5), 287–289 (1983)
Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nearest neighbours algorithm. IEEE Trans. Syst. Man Cybern 15(4), 580–585 (1985)
Derrac, J., Garcia, S., Herrera, F.: Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects. Information Sciences 260, 98–119 (2014)
Chen, H.-L., et al.: An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach. Expert Systems with Applications 40(1), 263–271 (2013)
Sim, J., Kim, S.Y., Lee, J.: Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method. Bioinformatics 21(12), 2844–2849 (2005)
Chen, H.-L., et al.: A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method. Knowledge-Based Systems 24(8), 1348–1359 (2011)
Cheng, M.-Y., Nhat-Duc, H.: Groutability Estimation of Grouting Processes with Microfine Cements Using an Evolutionary Instance-Based Learning Approach. Journal of Computing in Civil Engineering, 28(4) (2014)
Cheng, M.-Y., Hoang, N.-D.: A Swarm-Optimized Fuzzy Instance-based Learning approach for predicting slope collapses in mountain roads. Knowledge-Based Systems 76, 256–263 (2015)
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine 22(3), 52–67 (2002)
de Lau, L.M.L., Breteler, M.M.B.: Epidemiology of Parkinson’s disease. The Lancet Neurology 5(6), 525–535 (2006)
Singh, N., Pillay, V., Choonara, Y.E.: Advances in the treatment of Parkinson’s disease. Progress in neurobiology 81(1), 29–44 (2007)
Harel, B., Cannizzaro, M., Snyder, P.J.: Variability in fundamental frequency during speech in prodromal and incipient Parkinson’s disease: A longitudinal case study. Brain and Cognition 56(1), 24–29 (2004)
Baken, R.J., Orlikoff, R.F.: Clinical measurement of speech and voice, 2nd edn. Singular Publishing Group, San Diego, CA (2000)
Little, M.A., et al.: Suitability of Dysphonia Measurements for Telemonitoring of Parkinson’s Disease. IEEE Transactions on Biomedical Engineering 56(4), 1015–1022 (2009)
Das, R.: A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Systems with Applications 37(2), 1568–1572 (2010)
Shahbaba, B., Neal, R.: Nonlinear models using Dirichlet process mixtures. The Journal of Machine Learning Research 10, 1829–1850 (2009)
Psorakis, I., Damoulas, T., Girolami, M.A.: Multiclass Relevance Vector Machines: Sparsity and Accuracy. IEEE Transactions on Neural Networks 21(10), 1588–1598 (2010)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3) (2011)
Fawcett, T.: ROC graphs: Notes and practical considerations for researchers. Machine Learning 31, 1–38 (2004)
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Chen, H., Lu, J., Li, Q., Lou, C., Pan, D., Yu, Z. (2015). A New Evolutionary Fuzzy Instance-Based Learning Approach: Application for Detection of Parkinson’s Disease. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9141. Springer, Cham. https://doi.org/10.1007/978-3-319-20472-7_5
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DOI: https://doi.org/10.1007/978-3-319-20472-7_5
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