Analysis of Feature Selection Methods to Improve the Accuracy of SVM Algorithm in Classifying Complex Data

DOI

10.2991/978-94-6463-998-8_12How to use a DOI?

Keywords

Support Vector Machine (SVM); Feature Selection; Gain Ratio; Chi-Square; ReliefF; Accuracy

Abstract

This research aims to improve the accuracy of the Support Vector Machine (SVM) algorithm in classifying primary tumor data through feature selection. Primary tumors, which can be benign or malignant, require accurate diagnosis for effective medical treatment. By utilizing primary tumor datasets from UCI Machine Learning, this research applies three feature selection methods: Gain Ratio, Chi-Square, and ReliefF. After going through the preprocessing and feature selection stages, SVM is used to classify the data. The results show that without feature selection, the SVM accuracy is 74.3363%. Applying Gain Ratio and Chi-Square feature selection increases accuracy to 75.5162%, while the ReliefF method provides the highest increase to 75.8112%. Thus, the feature selection method is proven to be effective in improving SVM accuracy, with ReliefF as the most superior method. This research emphasizes the importance of feature selection in medical data processing to increase the accuracy of classification results.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jimi Firgo Dakhi
AU  - Jonathan Louis Cavalera Purba
AU  - Adrian Hutabarat
AU  - Cindy Sitanggang
AU  - Rico Wijaya Dewantoro
PY  - 2026
DA  - 2026/03/05
TI  - Analysis of Feature Selection Methods to Improve the Accuracy of SVM Algorithm in Classifying Complex Data
BT  - Proceedings of the 1st International Conference of Technology, Innovation, Design & Enterprise (ICTIDE 2025)
PB  - Atlantis Press
SP  - 89
EP  - 96
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-998-8_12
DO  - 10.2991/978-94-6463-998-8_12
ID  - Dakhi2026
ER  -