A hybrid Cancer Classification Based on SVM Optimized by PSO and Reverse Firefly Algorithm

  • Bibhuprasad Sahu, Amrutanshu Panigrahi, Sarita Mohanty, Satya Sobhan Panigrahi

Abstract

Feature selection is used by all researchers to identify the features of genes from the high dimensional dataset. It provides a pathway to identify the actual phase of the disease so that the accurate precaution can be considered to save a life. In this article, to identify the irrelevant as well as the redundant features we have adopted FCBF (First Correlation-based feature selection). Later we have considered SVM which is optimized by PSO and recursive FA(Firefly algorithm). Here the adopted method is known as PRFA-SVM. The proposed approach is applied to different well-known datasets publicly available in repositories. The comparison of classification accuracy states that the proposed PRFA-SVM approach provides effectiveness and robustness.

Published
2020-05-18
How to Cite
Bibhuprasad Sahu, Amrutanshu Panigrahi, Sarita Mohanty, Satya Sobhan Panigrahi. (2020). A hybrid Cancer Classification Based on SVM Optimized by PSO and Reverse Firefly Algorithm. International Journal of Control and Automation, 13(4), 506 - 517. Retrieved from https://sersc.org/journals/index.php/IJCA/article/view/16468
Section
Articles