Feature Selection Using Gram-Schmidt Orthogonalization For Support Vector Regression – A Case Study Of Mortality Rate Prediction Caused By Pneumonia

Feature selection is a technique for finding optimal features among original features by eliminating irrelevant features. Besides improving the learning accuracy and facilitate a better understanding of the model, feature selection may reduce the cost of building, storing and processing models. Recently, a Gram-Schmidt Orthogonalization-based feature selection is proposed for unstructured data. In this paper, we extend this Gram-Schmidt Orthogonalization-based feature selection for structured data. Our simulation shows that this Gram-Schmidt Orthogonalization-based feature selection improves the accuracy of Support Vector Regression in the average of 1.384925% for the case study of the prediction of mortality rates caused by pneumonia.