An Effective Filter Method Towards the Performance Improvement of FF-SVM Algorithm

Fining effective and informative biomarker genes form microarray is very challenging. In order to develop an hybrid gene selection algorithm, numerous filter feature selection algorithms have been previously reported. This research paper aims to identify the filter method that will improve the performance of our previously proposed FF-SVM algorithm to find the minimum number of accurate genes that achieves high accuracy performance. Therefore, an experiment was conducted using four different filter methods: Maximum Relevance Minimum Redundancy (mRMR), Joint Mutual Information (JMI), F-score, and Double Input Symmetrical Relevance (DISR). This experiment was undertaken in two phases: the first phase was filter to SVM, to identify the minimum number of features (genes) which served to maximize the SVM classifier; the second phase was filter to FF-SVM, to ascertain the best suite filter method to our previously proposed FF-SVM algorithm. The result of this experiment would be the most suited filter method to the FF-SVM. In conclusion, we found that the f-score method outperformed other filter methods when combined with FF-SVM.