| Authors | Iman Behravan, Oveis Dehghantanha, Seyed Hamid Zahiri |
|---|---|
| Conference Title | 1st Conference on Swarm Intelligence and Evolutionary Computation |
| Holding Date of Conference | 2016-03 |
| Event Place | Bam |
| Presentation | SPEECH |
| Conference Level | National Conferences |
| Keywords | Multi-objective optimization, particle swarm optimization, Pattern Recognition, Support Vector Machines |
Abstract
Support vector machine is a classifier, based on the
structured risk minimization principle. The performance of the
SVM, depends on different parameters such as: penalty factor,
C, and the kernel factor, σ. Also choosing an appropriate kernel
function can improve the Recognition Score and lower the
amount of computation. Furthermore, selecting the useful
features among several features in dataset not only increases the
performance of the SVM, but also reduces the computation time
and complexity. So this is an optimization problem which can be
solved by a heuristic algorithm. In some cases besides the
Recognition Score, the Reliability of the classifier’s output, is
important. So in such cases a multi-objective optimization
algorithm is needed. In this paper we have got the MOPSO
algorithm to optimize the parameters of the SVM, choose
appropriate kernel function and select the best features
simultaneously in order to optimize the Recognition Score and
the Reliability of the SVM. Nine different datasets, from UCI
machine learning repository, are used to evaluate the power and
the effectiveness of the proposed method (MOPSO-SVM). The
results of the proposed method are compared to those which are
achieved by RBF and MLP neural networks