基于生物入侵的特征选择算法

doi:10.19678/j.issn.1000-3428.0068627

摘要/Abstract

摘要：

在自然界中, 生物入侵以其发展的迅速和巨大的生态影响而受到关注, 所引入种群对合适栖息地的寻找过程往往有其内在的逻辑, 种群之间的交流和种群的扩张也在这个过程中起到了重要作用。通过探究种群对适宜栖息地的寻找原理, 提出一种基于生物入侵的特征选择(BIAFS)算法。在BIAFS算法中, 生物入侵过程分为种群建立、种群迁移、种群交流和扩张、种群发展4个阶段。在实验验证过程中, 在9个数据集上将BIAFS算法与8种高性能算法进行实验比较。实验结果显示, BIAFS算法在7个数据集上的分类准确率(CA)和降维(DR)率均超过了对比算法。此外, 适应度标准偏差的比较实验也证实了BIAFS算法的高稳定性, 表明其在多个数据集上能更加稳健地寻找最优解。上述实验结果证明了BIAFS算法在特征选择任务中的有效性和优越性。

关键词: 生物入侵, 特征选择, 入侵动态, 差分进化, 精英策略

Abstract:

In nature, biological invasions have attracted attention because of their rapid development and significant ecological impacts. The introduction of populations to search for suitable habitats often has inherent logic, and communication between populations and population expansion also play an important role in this process. A Biological Invasion-Based Feature Selection (BIAFS) algorithm is proposed by exploring the principle of population searches for suitable habitats. In the BIAFS algorithm, the biological invasion process is divided into four stages: population establishment, migration, communication and expansion, and development. During the experimental verification process, the BIAFS algorithm is compared with eight high-performance algorithms using nine datasets. The experimental results show that the Classification Accuracy (CA) and Dimensionality Reduction (DR) rate of the BIAFS algorithm on the seven datasets exceeded those of the comparison algorithm. In addition, a comparative experiment with the fitness standard deviation also confirms the high stability of the BIAFS algorithm, indicating that it can more robustly determine the optimal solution in multiple datasets. These experimental results demonstrate the effectiveness and superiority of the BIAFS algorithm for feature selection tasks.

Key words: biological invasion, feature selection, invasion dynamics, Differential Evolution(DE), elitism strategy

张健, 张博. 基于生物入侵的特征选择算法[J]. 计算机工程, 2024, 50(9): 46-53.

ZHANG Jian, ZHANG Bo. Biological Invasion-Based Feature Selection Algorithm[J]. Computer Engineering, 2024, 50(9): 46-53.

https://www.ecice06.com/CN/Y2024/V50/I9/46

图/表 5

参考文献 41

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