The Construction of Support Vector Machine Classifier Using the Firefly Algorithm
| Autor: | Chih-Feng Chao, Ming-Huwi Horng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Computer Science::Machine Learning
Support Vector Machine General Computer Science Article Subject Computer science General Mathematics Computer Science::Neural and Evolutionary Computation ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Feature selection lcsh:Computer applications to medicine. Medical informatics Machine learning computer.software_genre lcsh:RC321-571 Pattern Recognition Automated Multiclass classification Statistics::Machine Learning Artificial Intelligence Firefly algorithm Diagnosis Computer-Assisted lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Structured support vector machine business.industry General Neuroscience Particle swarm optimization Pattern recognition General Medicine Support vector machine ComputingMethodologies_PATTERNRECOGNITION Binary classification Computer Science::Sound Computer Science::Computer Vision and Pattern Recognition Hyperparameter optimization lcsh:R858-859.7 Artificial intelligence business computer Algorithms Research Article |
| Zdroj: | Computational Intelligence and Neuroscience Computational Intelligence and Neuroscience, Vol 2015 (2015) |
| ISSN: | 1687-5265 |
| DOI: | 10.1155/2015/212719 |
| Popis: | The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy. |
| Databáze: | OpenAIRE |
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