Improving golden jackel optimization algorithm: An application of chemical data classification

• Published in: Chemometrics and intelligent laboratory systems 2024-07, Vol.250, p.105149, Article 105149
• Main Authors: Alharthi, Aiedh Mrisi, Kadir, Dler Hussein, Al-Fakih, Abdo Mohammed, Algamal, Zakariya Yahya, Al-Thanoon, Niam Abdulmunim, Qasim, Maimoonah Khalid
• Format: Article
• Language: English
• Subjects: Chaotic maps; Chemometrics data; Feature selection; Golden jackal optimization; Quantitative structure-activity relationship (QSAR)
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2024.105149

One of the main issues affecting the effectiveness of the quantitative structure-activity relationship (QSAR) classification techniques in chemometrics is high dimensionality. Applying feature selection is a critical procedure that determines the most relevant and important aspects of a dataset. It improves the effectiveness and accuracy of prediction models by effectively lowering the number of features. This decrease increases classification accuracy, reduces computing strain, and improves overall performance. Recently, the golden jackal optimization (GJO) algorithm was introduced, which has been successfully used to solve various continuous optimization issues. Therefore, this study proposes an improvement in the GJO algorithm employing chaotic maps, abbreviated as CGJO, to enhance the exploration and exploitation capability of the GJO algorithm in picking the essential descriptors in QSAR classification models with high classification accuracy and less computation time. Experimental findings based on four different high-dimensional chemical datasets show that the proposed CGJO algorithm can maximize classification accuracy while simultaneously decreasing the number of chosen descriptors and lowering the time required for computing. Thus, the proposed algorithm can be useful for chemical data classification in other QSAR modeling. •This study proposes an improvement in the GJO algorithm employing chaotic maps.•We examined the performance of the CGJO for feature selection in classification.•The CGJO has better performance than GJO.•The classification ability for the CGJO is quite high.