.png){kind=icon}
返回
A self-adaptive level-based learning artificial bee colony algorithm for feature selection on high-dimensional classification
.png){kind=icon}
OA
AI
DOI：10.1007/s00500-022-06826-1.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 is an important data preprocessing method in data mining and machine learning, yet it faces the challenge of curse of dimensionality when dealing with high-dimensional data. In this paper, a self-adaptive level-based learning artificial bee colony (SLLABC) algorithm is proposed for high-dimensional feature selection problem. The SLLABC algorithm includes three new mechanisms: (1) A novel level-based learning mechanism is introduced to accelerate the convergence of the basic artificial bee colony algorithm, which divides the population into several levels and the individuals on each level learn from the individuals on higher levels, especially, the individuals on the highest level learn from each other. (2) A self-adaptive method is proposed to keep the balance between exploration and exploitation abilities, which takes the diversity of population into account to determine the number of levels. The lower the diversity is, the fewer the levels are divided. (3) A new update mechanism is proposed to reduce the number of selected features. In this mechanism, if the error rate of an offspring is higher than or is equal to that of its parent but selects more features, then the offspring is discarded and the parent is retained, otherwise, the offspring replaces its parent. Further, we discuss and analyze the contribution of these novelties to the diversity of population and the performance of classification. Finally, the results, compared with 8 state-of-the-art algorithms on 12 high-dimensional datasets, confirm the competitive performance of the proposed SLLABC on both classification accuracy and the size of the feature subset.
Keyword:
Artificial bee colony algorithm
Feature selection
Classification
High dimensionality
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
暂无机构信息
引用论文
分享收藏A survey on swarm intelligence approaches to feature selection in data mining数据挖掘中特征选择的群体智能方法综述
PREAI
分享收藏Variable-Length Particle Swarm Optimization for Feature Selection on High-Dimensional Classification高维分类特征选择的变长粒子群算法
OAAI
OAAI分享收藏A new ensemble feature selection approach based on genetic algorithm一种新的基于遗传算法的集成特征选择方法.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏Genome Sequences of Anelloviruses, Genomovirus, and Papillomavirus Isolated from Nasal Pharyngeal Swabs鼻咽拭子分离的Anelloviruses、Genomovirus和Papillomavirus基因组序列OAAI
OAAI分享收藏A hybrid approach of differential evolution and artificial bee colony for feature selection一种差分进化与人工蜂群混合的特征选择方法
PREAI
分享收藏
查看更多内容