Abstract
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.
Highlights
In many real-world classification tasks, reducing the dimensionality of data is an essential step before classifying the data
To optimize support vector machine (SVM)’s parameter setting, the key parameters must be optimized, such as the penalty parameter C, which controls the trade-off between model complexity and fitting error minimization, and the hyperplane parameter γ, which is the kernel bandwidth of the radial basis function (RBF), which should be properly optimized before performing classification tasks
The main contributions of this study are as follows: (1) it develops an improved fly optimization algorithm (FOA) based on chaotic particle optimization with a novel mutation strategy, and (2) it develops an improved FOA that is successfully applied to SVM to determine proper parameter settings with an optimal subset of features for real-world classification problems
Summary
In many real-world classification tasks, reducing the dimensionality of data is an essential step before classifying the data. To solve the medical data classification problem, [28] used FOA to optimize SVM by determining an optimal parameter setting of the SVM model; compared with other well-known methods, the results of the constructed experiments of this method showed that the optimized SVM model with FOA is a powerful tool for medical data classification. The above facts motivate us to propose a novel, intelligent framework using the proposed CIFOA and its improved mutation strategy, aiming to enhance the generalization ability and improve the classification performance of the SVM classifier by determining a proper parameter setting with an optimal feature subset simultaneously. The main contributions of this study are as follows: (1) it develops an improved FOA based on chaotic particle optimization with a novel mutation strategy, and (2) it develops an improved FOA that is successfully applied to SVM to determine proper parameter settings with an optimal subset of features for real-world classification problems.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have