[1]余紫康,董红斌.具有混合策略的樽海鞘群特征选择算法[J].智能系统学报,2024,19(3):757-765.[doi:10.11992/tis.202209040]
YU Zikang,DONG Hongbin.Salp swarm feature selection algorithm with a hybrid strategy[J].CAAI Transactions on Intelligent Systems,2024,19(3):757-765.[doi:10.11992/tis.202209040]
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YU Zikang,DONG Hongbin.Salp swarm feature selection algorithm with a hybrid strategy[J].CAAI Transactions on Intelligent Systems,2024,19(3):757-765.[doi:10.11992/tis.202209040]
具有混合策略的樽海鞘群特征选择算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第3期
页码:
757-765
栏目:
学术论文—人工智能基础
出版日期:
2024-05-05
- Title:
- Salp swarm feature selection algorithm with a hybrid strategy
- Keywords:
- feature selection; salp swarm algorithm; transient search algorithm; heuristic algorithm; mutual information; dynamic search algorithm; rank sum test; K-nearest neighbor
- 分类号:
- TP301
- 文献标志码:
- 2023-09-15
- 摘要:
- 近年来，随着计算机和数据库技术的快速发展，大规模数据集迅速增长，利用特征选择技术来筛选信息量大的特征已经变得非常重要。本文提出了一种具有混合策略的樽海鞘群特征选择算法(salp swarm feature selection algorithm with hybrid strategy，HS-SSA)。首先，本文生成一张基于互信息的排序表，并由排序表提出了新的初始化策略。其次，提出一个新颖的并且有条件调用的动态搜索算法。最后在位置更新上结合瞬态搜索算法(transient search algorithm, TSO)，改进勘探和开发步骤的效率，增加解空间的灵活性和多样性，从而使算法能够快速定位到全局最优位置。为了验证算法的性能，实验选取14个UCI的数据集，并且与樽海鞘群算法(SSA)以及近几年樽海鞘群的改进算法等多种优化算法进行比较，结果表明HS-SSA在特征选择上具有更强的竞争力。
- Abstract:
- In recent years, with the rapid development of computer and database technologies, the number of large-scale datasets has rapidly increased. Thus, the use of feature selection technology is important to screen features with massive amounts of information. In this study, a salp swarm feature selection algorithm with a hybrid strategy (HS-SSA) is proposed. Initially, a sorted table based on mutual information is generated, and a new initialization strategy is proposed on the basis of this sorted table. Furthermore, a novel dynamic search algorithm with conditional call is proposed. With respect to location updates, the efficiency of exploration and development steps is improved, and the flexibility and diversity of the solution space are increased by combining the transient search algorithm (TSO). Consequently, the algorithm can rapidly locate the global optimal location. To verify algorithm performance, 14 UCI datasets were selected for the test. In addition, the proposed algorithm was compared with the salp swarm algorithm (SSA), the improved SSA, and many other improved algorithms in recent years. The results show that HS-SSA is more competitive in feature selection.
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备注/Memo
收稿日期:2022-09-19。
基金项目:黑龙江自然科学基金项目(LH2020F023).
作者简介:余紫康，硕士研究生，主要研究方向是群智能算法、数据挖掘。E-mail：y402153832@163.com;董红斌，教授，博士生导师，中国计算机学会高级会员，主要研究方向为多智能体系统、机器学习。主持和完成国家自然科学基金、工信部基础研究项目、黑龙江省自然科学基金项目，荣获黑龙江省高校科学技术奖和黑龙江省优秀高等教育科学成果奖。主编教材2部，发表学术论文90余篇。E-mail：donghongbin@hrbeu.edu.cn
通讯作者:董红斌. E-mail：donghongbin@hrbeu.edu.cn
更新日期/Last Update:
1900-01-01