.png){kind=icon}
返回
A Novel Binary Seagull Optimizer and its Application to Feature Selection Problem
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3098642.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Seagull Optimization Algorithm (SOA) is a metaheuristic algorithm that mimics the migrating and hunting behaviour of seagulls. SOA is able to solve continuous real-life problems, but not to discrete problems. The eight different binary versions of SOA are proposed in this paper. The proposed algorithm uses four transfer functions, S-shaped and V-shaped, which are used to map the continuous search space into discrete search space. Twenty-five benchmark functions are used to validate the performance of the proposed algorithm. The statistical significance of the proposed algorithm is also analysed. Experimental results divulge that the proposed algorithm outperforms the competitive algorithms. The proposed algorithm is also applied on data mining. The results demonstrate the superiority of binary seagull optimization algorithm in data mining application.
Keyword:
Transfer functions
Feature extraction
Spirals
Search problems
Statistics
Sociology
Hypercubes
Seagull algorithm
feature selection
discrete optimization
nature-inspired algorithms
exploration
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Binary dragonfly optimization for feature selection using time-varying transfer functions使用时变传递函数进行特征选择的二进制蜻蜓优化OAAI
OAAI分享收藏BEPO: A novel binary emperor penguin optimizer for automatic feature selectionBEPO: 一种用于自动特征选择的新型二进制帝企鹅优化器
PREAI
分享收藏分享收藏Loss of heterozygosity in three embryonal tumours suggests a common pathogenetic mechanism.png)
Nature
NatureIF0
PREAI
分享收藏Changes in non-cellulosic cell-wall polysaccharides during the growth of carrot cells in suspension culturesPlanta
PlantaIF0
PREAI
分享收藏Binary grey wolf optimization approaches for feature selection基于二进制灰狼优化的特征选择方法NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容