E ISSN: 2583-049X

International Journal of Advanced Multidisciplinary Research and Studies

Volume 3, Issue 4, 2023

Enhanced Binary Tree Growth Algorithm with Linear Discriminant Analysis for Leukemia Dataset Classification

Author(s): Suzan Muhsen Al-Saffar, Omar S Qasim


In this study, a novel meta-heuristic method named the Quasi-Opposition-Based Learning Tree Growth Algorithm (QOBL-TGA) is introduced to overcome the issues of slow convergence and local optima commonly associated with the original Tree Growth Algorithm (TGA). By combining the quasi-opposition (QOBL) and opposition-based learning (OBL) techniques, the QOBL-TGA approach enhances the global search capabilities of TGA. To assess its effectiveness, the QOBL-TGA is applied to the leukemia dataset using the Linear Discriminant Analysis (LDA) classifier. The results demonstrate that the QOBL-TGA approach surpasses the original TGA and other existing algorithms, exhibiting faster convergence and superior optimization performance. In summary, the QOBL-TGA method presents a valuable solution for addressing optimization challenges in high-dimensional problems.

Keywords: Feature Selection, Classification, Quasi Opposition-Based Learning, Tree Growth Algorithm, Leukemia

Pages: 943-947

Download Full Article: Click Here