.png){kind=icon}
返回
A novel binary horse herd optimization algorithm for feature selection problem
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-023-15023-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Feature selection (FS) is an essential step for machine learning problems that can improve the performance of the classification by removing useless features from the data set. FS is an NP-hard problem, so meta-heuristic algorithms can be used to find good solutions for this problem. Horse herd Optimization Algorithm (HOA) is a new meta-heuristic approach inspired by horses 'herding behavior. In this paper, an improved version of the HOA algorithm called BHOA is proposed as a wrapper-based FS method. To convert continuous to discrete search space, S-Shaped and V-Shaped transfer functions are considered. Moreover, to control selection pressure, exploration, and exploitation capabilities, the Power Distance Sums Scaling approach is used to scale the fitness values of the population. The efficiency of the proposed method is estimated on 17 standard benchmark datasets. The implementation results prove the efficiency of the proposed method based on the V-shaped category of transfer functions compared to other transfer functions and other wrapper-based FS algorithms.
Keyword:
Horse herd optimization algorithm (HOA)
Binary horse herd optimization
Transfer function
Feature selection
Classification
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Binary grey wolf optimization approaches for feature selection基于二进制灰狼优化的特征选择方法.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
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
分享收藏分享收藏分享收藏
Multi-strategy ensemble grey wolf optimizer and its application to feature selection多策略集成灰狼优化器及其在特征选择中的应用
PREAI
分享收藏
查看更多内容