进化计算在大规模高维特征选择中的应用综述
Review of Large-scale High-dimensional Feature Selection Based on Evolutionary Computation
叶志伟 1王巧 1周雯 1王明威 1蔡婷 1何其祎1
作者信息
- 1. 湖北工业大学 计算机学院,武汉430068
- 折叠
摘要
随着大数据时代的到来,数据的规模和特征维度呈现爆炸式增长,这给数据处理带来了前所未有的挑战.特征选择作为数据预处理的关键环节,在处理大规模高维数据时显得尤为重要.而进化计算方法因其出色的全局搜索能力和高效的优化性能,越来越多的研究者开始对其进行研究,其在大规模高维特征选择中得到了广泛的应用.本文首先介绍了大规模高维数据处理的重要性;然后简单介绍了部分经典和较新的进化计算方法,并详细介绍了其在大规模高维特征选择中的应用情况;最后对目前进化计算在大规模高维特征选择中存在的问题进行总结,并展望了其未来的发展方向.
Abstract
With the advent of the big data era, the scale and feature dimensions of data show explosive growth, which brings unprecedented challenges to data processing.Feature selection, as a key link in data preprocessing, is particularly important when processing large-scale high-dimensional data.Due to its excellent global search capabilities and efficient optimization performance, more and more researchers begin to study the evolutionary computing method, and it is widely used in large-scale high-dimensional feature selection.This paper first introduces the importance of large-scale high-dimensional data processing.Then some classic and newer evolutionary calculation methods are briefly introduced, and their applications in large-scale high-dimensional feature selection are introduced in detail.Finally, the application of evolutionary computing in large-scale high-dimensional feature selection is summarized and its future development direction is prospected.
关键词
特征选择/进化计算/全局搜索/数据预处理/机器学习Key words
feature selection/evolutionary computation/global search/data preprocessing/machine learning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据