Abstract
In this paper, we present a new swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization algorithm (MFOA). The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QMFOA has a simple two-phase approach, the first phase is a preprocessing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is hybridization among MFOA, quantum computing, and support vector machine (SVM) with leave-one-out cross-validation (LOOCV), in order to solve the gene selection problem. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test it on six Microarray datasets. Experimental results show that QMFOA provides great classification accuracy in comparison to some known algorithms.
Recommended Citation
Dabba, Ali; Tari, Abdelkamel; and Meftali, Samy
(2024)
"Gene Selection and Classification Using Quantum Moth Flame Optimization Algorithm,"
American Journal of Science & Engineering (AJSE): Vol. 1:
Iss.
2, Article 4.
Available at:
https://research.smartsociety.org/ajse/vol1/iss2/4