Zhang Kunpeng, Liu Yanheng, Mei Fang, Sun Geng, Jin Jingyi
College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun 130012, China.
Entropy (Basel). 2023 Jul 27;25(8):1128. doi: 10.3390/e25081128.
Feature selection is a crucial process in machine learning and data mining that identifies the most pertinent and valuable features in a dataset. It enhances the efficacy and precision of predictive models by efficiently reducing the number of features. This reduction improves classification accuracy, lessens the computational burden, and enhances overall performance. This study proposes the improved binary golden jackal optimization (IBGJO) algorithm, an extension of the conventional golden jackal optimization (GJO) algorithm. IBGJO serves as a search strategy for wrapper-based feature selection. It comprises three key factors: a population initialization process with a chaotic tent map (CTM) mechanism that enhances exploitation abilities and guarantees population diversity, an adaptive position update mechanism using cosine similarity to prevent premature convergence, and a binary mechanism well-suited for binary feature selection problems. We evaluated IBGJO on 28 classical datasets from the UC Irvine Machine Learning Repository. The results show that the CTM mechanism and the position update strategy based on cosine similarity proposed in IBGJO can significantly improve the Rate of convergence of the conventional GJO algorithm, and the accuracy is also significantly better than other algorithms. Additionally, we evaluate the effectiveness and performance of the enhanced factors. Our empirical results show that the proposed CTM mechanism and the position update strategy based on cosine similarity can help the conventional GJO algorithm converge faster.
特征选择是机器学习和数据挖掘中的一个关键过程，它能识别数据集中最相关且最有价值的特征。通过有效减少特征数量，它提高了预测模型的有效性和精度。这种减少提高了分类准确率，减轻了计算负担，并提升了整体性能。本研究提出了改进的二进制金豺优化（IBGJO）算法，它是传统金豺优化（GJO）算法的扩展。IBGJO用作基于包装器的特征选择的搜索策略。它包含三个关键因素：一种带有混沌帐篷映射（CTM）机制的种群初始化过程，该机制增强了开发能力并保证了种群多样性；一种使用余弦相似度的自适应位置更新机制，以防止过早收敛；以及一种非常适合二进制特征选择问题的二进制机制。我们在来自加州大学欧文分校机器学习库的28个经典数据集上对IBGJO进行了评估。结果表明，IBGJO中提出的CTM机制和基于余弦相似度的位置更新策略可以显著提高传统GJO算法的收敛速度，并且其准确率也明显优于其他算法。此外，我们评估了增强因素的有效性和性能。我们的实证结果表明，所提出的CTM机制和基于余弦相似度的位置更新策略可以帮助传统GJO算法更快地收敛。
