Sharma Ravi, Sharma Kapil, Bala Manju
Delhi Technological University, Bawana, New Delhi, 110042, India.
Indraprastha College of Women, University of Delhi, Civil Lines, New Delhi, 110054, India.
Sci Rep. 2024 Oct 24;14(1):25163. doi: 10.1038/s41598-024-75842-y.
The difficulty of selecting features efficiently in histopathology image analysis remains unresolved. Furthermore, the majority of current approaches have approached feature selection as a single objective issue. This research presents an enhanced multi-objective whale optimisation algorithm-based feature selection technique as a solution. To mine optimal feature sets, the suggested technique makes use of a unique variation known as the enhanced multi-objective whale optimisation algorithm. To verify the optimisation capability, the suggested variation has been evaluated on 10 common multi-objective CEC2009 benchmark functions. Furthermore, by comparing five classifiers in terms of accuracy, mean number of selected features, and calculation time, the effectiveness of the suggested strategy is verified against three other feature-selection techniques already in use. The experimental findings show that, when compared to the other approaches under consideration, the suggested method performed better on the assessed parameters.
在组织病理学图像分析中高效选择特征的难题仍未得到解决。此外，当前大多数方法将特征选择视为单一目标问题。本研究提出一种基于增强多目标鲸鱼优化算法的特征选择技术作为解决方案。为挖掘最优特征集，所提出的技术利用了一种名为增强多目标鲸鱼优化算法的独特变体。为验证优化能力，已在10个常见的多目标CEC2009基准函数上对所提出的变体进行了评估。此外，通过在准确率、所选特征的平均数量和计算时间方面比较五个分类器，针对其他三种已在使用的特征选择技术验证了所提出策略的有效性。实验结果表明，与所考虑的其他方法相比，所提出的方法在评估参数上表现更好。
