The Performance of Correlation-Based Support Vector Machine in Illiteracy Dataset

Conference paper (2018)

Authors

Indra Gunawan State University of Malang
Triyanna Widyaningtyas State University of Malang
Aji Prasetya Wibawa State University of Malang
undefined Haviluddin Haviluddin Mulawarman University
D. Darusalam Information and Communication Technology - TPM
Andri Pranolo Hohai University
Research Group
Information and Communication Technology (TPM) (TU Delft)
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Published Date

2018

Language

English

Reuse Rights

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Faculty

Technology, Policy and Management

Department

Engineering, Systems and Services

Research Group

Information and Communication Technology

Abstract

SVM method performs a non-linear mapping of original data space into a high-dimensional feature space. The construction of linear discrimination function is useful for replacing the non-linear function in the original data space. This paper aims to efficiently explore the accuracy of SVM with the feature selection method. The selected feature selection method is Correlation-based Feature Selection (CFS), due to the approach's simplicity and speed. This research used an illiteracy rate dataset in Indonesia. The research result showed that the optimised method has overcome the original SVM, with 94 % of accuracy.

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