hybrid coevolutionary algorithms vs. svm algorithms
As a learning method support vector machine is regarded as one of the best classifiers with a strong mathematical foundation. On the other hand, evolutionary computational technique is characterized as a soft computing learning method with its roots in the theory of evolution. During the past decade, SVM has been commonly used as a classifier for various applications. The evolutionary computation has also attracted a lot of attention in pattern recognition and has shown significant performance improvement on a variety of applications. However, there has been no comparison of the two methods. In this paper, first we propose an improvement of a coevolutionary computational classification algorithm, called Improved Coevolutionary Feature Synthesized EM (I-CFS-EM) algorithm. It is a hybrid of coevolutionary genetic programming and EM algorithm applied on partially labeled data. It requires less labeled data and it makes the test in a lower dimension, which speeds up the testing. Then, we provide a comprehensive comparison between SVM with different kernel functions and I-CFS-EM on several real datasets. This comparison shows that I-CFS-EM outperforms SVM in the sense of both the classification performance and the computational efficiency in the testing phase. We also give an intensive analysis of the pros and cons of both approaches.
support vector machines co-evolution machine learning pattern classification