An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications

• Published in: PloS one Vol. 12; no. 4; p. e0173516
• Main Authors: Ye, Fei, Lou, Xin Yuan, Sun, Lin Fu
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.04.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Animals; Artificial intelligence; Bayesian analysis; Biogeography; Biology and Life Sciences; Biomedical engineering; Brain; Classification; Computational neuroscience; Computer and Information Sciences; Computer applications; Cybernetics; Data processing; Databases, Factual; Design optimization; Diagnosis, Computer-Assisted; Drosophila - genetics; Drosophila - physiology; Energy conversion; Engineering; Expert systems; Feeding Behavior; Fitness; Food; Food sources; Foraging behavior; Fractals; Fruit flies (Tephritidae); Fruits; Gene expression; Gene mutation; Genetic aspects; Humans; Information science; Learning algorithms; Mammography; Mathematical models; Mathematical problems; Medical diagnosis; Medicine and Health Sciences; Movement disorders; Mutation; Neural networks; Neurodegenerative diseases; Neuroimaging; Noise; Nonlinear Dynamics; Optimization; Optimization algorithms; Optimization theory; Parameters; Parkinson's disease; Pattern recognition; Physical Sciences; Reproductive fitness; Research and Analysis Methods; Resonance; Risk reduction; Scaling; Science; Sensors; Simulation; Social Sciences; Solution space; Support Vector Machine; Support vector machines; Technology; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0173516

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm's performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.