Abstract
In this paper, we present a fruit fly optimization algorithm (FOA) based support vector machine (SVM) classification scheme, termed as FOA-SVM, and it is applied successfully to Parkinson’s disease (PD) diagnosis. In the proposed FOA-SVM, the set of parameters in SVM is tackled efficiently by the FOA technique. The effectiveness and efficiency of FOA-SVM has been rigorously evaluated against the PD dataset by comparing with the particle swarm optimization algorithm (PSO) optimized SVM (PSO-SVM), and grid search technique based SVM (Grid-SVM). The experimental results demonstrate that the proposed approach outperforms the other two counterparts in terms of diagnosis accuracy as well as the fewer CPU time. Promisingly, the proposed method can be regarded as a useful clinical decision tool for the physicians.
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Shen, L., Chen, H., Kang, W., Gu, H., Zhang, B., Ge, T. (2015). Fruit Fly Optimization Algorithm Based SVM Classifier for Efficient 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_11
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DOI: https://doi.org/10.1007/978-3-319-20472-7_11
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