SVM Parameter Optimization Using Grid Search and Genetic Algorithm to Improve Classification Performance
Abstract: 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 muchbetter 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.
Author: Iwan Syarif, Adam
Prugel-Bennett , Gary Wills
Journal Code: jptkomputergg160149
