Journal
KNOWLEDGE-BASED SYSTEMS
Volume 96, Issue -, Pages 61-75Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2016.01.002
Keywords
Support vector machine; Parameter optimization; Fruit fly optimization; Medical diagnosis
Categories
Funding
- National Natural Science Foundation of China (NSFC) [61303113, 61133011, 61572226, 61373053, 61373166, 61402336]
- Science and Technology Plan Project of Wenzhou of China [G20140048]
- Jilin Province Natural Science Foundation [20150101052JC]
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University [93K172013K01]
- open project program of Wenzhou University Laboratory [15SK26A]
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In this paper, a new support vector machines (SVM) parameter tuning scheme that uses the fruit fly optimization algorithm (FDA) is proposed. Termed as FOA-SVM, the scheme is successfully applied to medical diagnosis. In the proposed FOA-SVM, the FOA technique effectively and efficiently addresses the parameter set in SVM. Additionally, the effectiveness and efficiency of FOA-SVM is rigorously evaluated against four well-known medical datasets, including the Wisconsin breast cancer dataset, the Pima Indians diabetes dataset, the Parkinson dataset, and the thyroid disease dataset, in terms of classification accuracy, sensitivity, specificity, AUC (the area under the receiver operating characteristic (ROC) curve) criterion, and processing time. Four competitive counterparts are employed for comparison purposes, including the particle swarm optimization algorithm-based SVM (PSO-SVM), genetic algorithm-based SVM (GA-SVM), bacterial forging optimization-based SVM (BFO-SVM), and grid search technique-based SVM (Grid-SVM). The empirical results demonstrate that the proposed FOA-SVM method can obtain much more appropriate model parameters as well as significantly reduce the computational time, which generates a high classification accuracy. Promisingly, the proposed method can be regarded as a useful clinical tool for medical decision making. (C) 2016 Elsevier B.V. All rights reserved.
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