.png){kind=icon}
返回
Binary Symbiotic Organism Search Algorithm for Feature Selection and Analysis
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2953800.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Feature selection is a challenging step in the field of data mining, because there are many local optimal solutions in a feature space. Feature selection can be considered an optimization problem, which requires as few feature combinations as possible and high accuracy. The binary symbiotic organism search (BSOS) algorithm is proposed in this paper. It maps the symbiotic organism search algorithm from a continuous space to a discrete space using an adaptive S-shaped transfer function and can be used to search for the optimal feature subset in a feature selection space. The proposed BSOS algorithm is evaluated using 19 datasets from the UCI repository. First, the results of four basic S-shaped transfer functions are compared with those of the adaptive S-shaped transfer function. Additionally, the experimental results are compared with the results obtained by the popular binary grasshopper optimization, binary gray wolf optimization, traditional binary particle swarm optimization, and binary differential evolution algorithms, which are also employed for feature selection in the existing literature. The experimental results show that the BSOS algorithm can find the fewest number of features in most datasets and achieve a high classification accuracy. Moreover, the experiments also show that the BSOS algorithm is still at a disadvantage in handling low-dimensional datasets and attains low sensitivity in hyperdimensional datasets.
Keyword:
Symbiotic organism search
binary symbiotic organism search
feature selection
feature analysis
metaheuristic optimization
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
Genome-wide identification of turnip mosaic virus-responsive microRNAs in non-heading Chinese cabbage by high-throughput sequencing.png)
Gene
GeneIF0
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
分享收藏分享收藏
查看更多内容