Optimization Naive Bayes Algorithm Using Particle Swarm Optimization in the Classification of Breast Cancer
- DOI
- 10.2991/aisr.k.200424.055How to use a DOI?
- Keywords
- classification data mining, Naive Bayes, Particle Swarm Optimization, breast cancer, feature selection
- Abstract
Methods of data mining classification are used in various fields of research. Naive Bayes is one of the most used algorithms of data mining classification, especially in the medical science because Naive Bayes is considered good method for the data concerned with a statistical diagnosis. Optimization of diagnosis results needs to be done in terms of various weaknesses, including data passing certain classes even though the data it is irrelevant or relevant so the need to be optimized by feature selection. Optimization was done using Particle Swarm Optimazation algorithm for feature selection in breast cancer classification using Naive Bayes. The Naive Bayes method is used for the classification of breast cancer, while the Particle Swarm Optimization Algorithm is used for the selection of irrelevant attribute features in order to obtain optimal diagnosis results. The results of the Naive Bayes method were 95.49% while after being optimized with the Particle Swarm Optimazation Algorithm the result was 98.19%.
- Copyright
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Vira MELINDA AU - Rifkie PRIMARTHA AU - Adi WIJAYA AU - Muhammad Ihsan JAMBAK PY - 2020 DA - 2020/05/06 TI - Optimization Naive Bayes Algorithm Using Particle Swarm Optimization in the Classification of Breast Cancer BT - Proceedings of the Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019) PB - Atlantis Press SP - 362 EP - 369 SN - 1951-6851 UR - https://doi.org/10.2991/aisr.k.200424.055 DO - 10.2991/aisr.k.200424.055 ID - MELINDA2020 ER -