.png){kind=icon}
返回
MFeature: Towards high performance evolutionary tools for feature selection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2021.115655.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 commonly refers to a process of using the candidate algorithm to detect the optimal feature sets during the preprocessing steps in machine learning and data mining. This procedure can optimize the dataset's features to be analyzed and maximize the classification performance based on the selected optimal feature combination. In this work, a hybridization model is developed and utilized to select optimal feature subset based on an innovative binary version of moth-flame optimizer and the K-Nearest Neighbor Classifier (KNN) for classification tasks. The proposed new technique, abbreviated as MFeature or ESAMFO, applies several strategies, including two types of transfer functions, ensemble strategy, simulated annealing (SA) disturbance mechanism, and crossover scheme to improve the equilibrium between the global exploration and local exploitation capabilities of the basic MFO. Each individual in the proposed algorithm is evaluated by the size of selected features and the error rate of the KNN classifier. The proposed model's efficacy is assessed on 30 benchmark datasets with different dimensions from the UCI repository and compared with other KNN based feature selection algorithms from the literature. The comprehensive results via various comparisons reveal the efficiency of the proposed technique in decreasing the classification error rate compared with other feature selection algorithms, ensuring the capability of ESAMFO in exploring the feature space and selecting the most informative features for classification purposes. For post publications that support this research, readers can refer to https://aliasgharheidari. com.
Keyword:
Moth-flame optimization algorithm
Crossover
Ensemble
Simulated annealing
Feature selection
Data 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:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm蛾焰优化算法: 一种新的自然启发启发式范式
PREAI
分享收藏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Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies多种群差分进化辅助Harris hawks优化: 框架与案例研究
PREAI
分享收藏Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting具有特征选择技术和改进元启发式算法的两阶段预测引擎，用于电力负荷预测.png)
ENERGY
ENERGYIF9.4
PREAI
分享收藏分享收藏Effects of microorganism-mediated inoculants on humification processes and phosphorus dynamics during the aerobic composting of swine manure微生物菌剂对猪粪好氧堆肥腐殖化过程及磷动态的影响
PREAI
分享收藏
查看更多内容