The construction of support vector machine classifier using the firefly algorithm

Chih Feng Chao, Ming Huwi Horng

研究成果: Article同行評審

54 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號212719
期刊Computational Intelligence and Neuroscience
2015
DOIs
出版狀態Published - 2015 2月 23

All Science Journal Classification (ASJC) codes

  • 一般電腦科學
  • 一般神經科學
  • 一般數學

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