In this article, a new algorithm to select the relevant features is proposed for handling microarray data with the specific aim of increasing classification accuracy. In particular, the optimal genes are extracted using filter and wrapper feature selection algorithms. Here, the use of non-parametric regression algorithm called Multivariate Adaptive Regression Spline (MARS) followed by proposed Random Forest Statistical Test (RFST) algorithm are being studied. The study evaluates the comparative performance of the results of RFST and MARS with existing algorithms on ten standard microarray datasets. For performance analysis, three parameters are taken into consideration, namely, the number of selected features, runtime, and classification accuracy. Experimental results indicate that different feature selection algorithms yield different candidate gene subset; therefore, a Hybrid approach is applied to determine the best candidate genes to provide maximum information about the disease. The findings foretell that the RFST is performing better on six out of ten datasets whereas MARS is performing better on other datasets.
Evaluation of Logistic Regression and Multivariate Adaptive Regression Spline Models for Groundwater Potential Mapping Using R and GIS
