SVM Parameter Optimization using Grid Search and Genetic Algorithm to Improve Classification Performance

Machine Learning algorithms have been widely used to solve various kinds of data classification problems. Classification problem especially for high dimensional datasets have attracted many researchers in order to find efficient approaches to address them. However, the classification problem has become very complicated and computationally expensive, especially when the number of possible different combinations of variables is so high. Support Vector Machine (SVM) has been proven to perform much better when dealing with high dimensional datasets and numerical features. Although SVM works well with default value, the performance of SVM can be improved significantly using parameter optimization. We applied two methods which are Grid Search and Genetic Algorithm (GA) to optimize the SVM parameters. Our experiment showed that SVM parameter optimization using grid search always finds near optimal parameter combination within the given ranges. However, grid search was very slow; therefore it was very reliable only in low dimensional datasets with few parameters. SVM parameter optimization using GA can be used to solve the problem of grid search. GA has proven to be more stable than grid search. Based on average running time on 9 datasets, GA was almost 16 times faster than grid search. Futhermore, the GA’s results were slighlty better than the grid search in 8 of 9 datasets.