Evaluation of Cancer Classification Using Combined Algorithms with Support Vector Machines

Support vector machine (SVM) is a supervised learning method, which has considerable applications. It shows excellent performance in many pattern recognition applications. Also, combining SVMs with other theories has been proposed as a new direction to improve classification performance. Thus, in this paper some important aspects to reach the best performance in combined algorithms with SVM for cancer classification are explained. Since delay and accuracy are the important parameters to improve the performance in SVMs, some of the methods with these parameters are compared to use the best algorithms in the future works. Finally some directions for researches are provided.

Keywords:

Classification , Kernel functions , Machine learning , Support vector machine , Pattern recognition