.png){kind=icon}
返回
Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3000040.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Feature selection or dimensionality reduction can be considered as a multi-objective minimization problem with two objectives: minimizing the number of features and minimizing the error rate simultaneously. Despite being a multi-objective problem, most existing approaches treat feature selection as a single-objective optimization problem. Recently, Multi-objective Grey Wolf optimizer (MOGWO) was proposed to solve multi-objective optimization problem. However, MOGWO was originally designed for continuous optimization problems and hence, it cannot be utilized directly to solve multi-objective feature selection problems which are inherently discrete in nature. Therefore, in this research, a binary version of MOGWO based on sigmoid transfer function called BMOGW-S is developed to optimize feature selection problems. A wrapper based Artificial Neural Network (ANN) is used to assess the classification performance of a subset of selected features. To validate the performance of the proposed method, 15 standard benchmark datasets from the UCI repository are employed. The proposed BMOGWO-S was compared with MOGWO with a tanh transfer function and Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO). The results showed that the proposed BMOGWO-S can effectively determine a set of non-dominated solutions. The proposed method outperforms the existing multi-objective approaches in most cases in terms of features reduction as well as classification error rate while benefiting from a lower computational cost.
Keyword:
Feature selection
grey wolf optimizer
multi-objective optimization
classification
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R2D2, a Bridge Between the Initiation and Effector Steps of the
Drosophila
RNAi Pathway.png)
Science
ScienceIF0
PREAI
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Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm基于双存档多目标人工蜂群算法的代价敏感特征选择
PREAI
分享收藏
查看更多内容