基于Harris Hawks优化和Salp Swarm优化的COVID-19混合分类方法
An efficient hybrid classification approach for COVID-19 based on Harris Hawks Optimization and Salp Swarm Optimization
摘要：
特征选择可以定义为预处理步骤之一，其通过识别最重要的属性来降低数据集的维度，同时还提高分类的准确性。为了解决特征选择问题，本研究提出了一种用于Covid-19分类的Harris Hawks优化算法（HHO）和Salp Swarm优化算法（SSA）（HHOSSA）的混合二进制版本。建议（HHOSSA）提出了一种策略，用于改善基本HHO的性能，使用Salp算法的权力，以选择最佳的健身值。HHOSSA针对两种众所周知的优化算法进行了测试，鲸鱼优化算法（WOA）和灰狼优化器（GWO），共使用了800张胸部X射线图像。在使用三种分类器（支持向量机（SVM）、k-最近邻（KNN）和极端梯度提升（XGBoost））的研究中，总共采用了四种性能指标（准确度、召回率、精度、F1）。所提出的算法（HHOSSA）实现了96%的准确率与SVM分类器，和98%的准确率与两个分类器，XGboost和KNN。
英文摘要：
Feature selection can be defined as one of the pre-processing steps that decrease the dimensionality of a dataset by identifying the most significant attributes while also boosting the accuracy of classification. For solving feature selection problems, this study presents a hybrid binary version of the Harris Hawks Optimization algorithm (HHO) and Salp Swarm Optimization (SSA) (HHOSSA) for Covid-19 classification. The proposed (HHOSSA) presents a strategy for improving the basic HHO's performance using the Salp algorithm's power to select the best fitness values. The HHOSSA was tested against two well-known optimization algorithms, the Whale Optimization Algorithm (WOA) and the Grey wolf optimizer (GWO), utilizing a total of 800 chest X-ray images. A total of four performance metrics (Accuracy, Recall, Precision, F1) were employed in the studies using three classifiers (Support vector machines (SVMs), k-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost)). The proposed algorithm (HHOSSA) achieved 96% accuracy with the SVM classifier, and 98% accuracy with two classifiers, XGboost and KNN.
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