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Feature Selection Using Gram-Schmidt Orthogonalization For Support Vector Regression – A Case Study Of Mortality Rate Prediction Caused By Pneumonia
| Content Provider | Scilit |
|---|---|
| Author | Dewi, Yuni R. Murfi, Hendri |
| Copyright Year | 2019 |
| Description | Journal: Journal of Physics: Conference Series 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. |
| Related Links | https://iopscience.iop.org/article/10.1088/1742-6596/1192/1/012004/pdf |
| ISSN | 17426588 |
| e-ISSN | 17426596 |
| DOI | 10.1088/1742-6596/1192/1/012004 |
| Journal | Journal of Physics: Conference Series |
| Issue Number | 1 |
| Volume Number | 1192 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2019-03-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of Physics: Conference Series Industrial Engineering Gram Schmidt Orthogonalization Feature Selection |
| Content Type | Text |
| Resource Type | Article |
| Subject | Physics and Astronomy |
