Feature Selection Using Gram-Schmidt Orthogonalization for Support Vector Regression - A Case Study of Mortality Rate Prediction Caused by Pneumonia

Yuni R. Dewi, Hendri Murfi

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish
Article number012004
JournalJournal of Physics: Conference Series
Volume1192
Issue number1
DOIs
Publication statusPublished - 17 May 2019
Event2nd International Conference on Data and Information Science, ICoDIS 2018 - Bandung, Indonesia
Duration: 15 Nov 201816 Nov 2018

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