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.
|Journal||Journal of Physics: Conference Series|
|Publication status||Published - 17 May 2019|
|Event||2nd International Conference on Data and Information Science, ICoDIS 2018 - Bandung, Indonesia|
Duration: 15 Nov 2018 → 16 Nov 2018