基于互信息和遗传算法的特征选择算法
Feature Selection Algorithm Based on Mutual Information and Genetic Algorithm
张婧 1曹峰 2董毓莹 2张超 2余银中 3唐超4
作者信息
- 1. 太原学院 数学系,山西 太原 030032
- 2. 山西大学 计算机与信息技术学院,山西 太原 030006
- 3. 安徽华治新能源科技有限公司,安徽 合肥 230601
- 4. 合肥学院 人工智能与大数据学院,安徽 合肥 230601
- 折叠
摘要
本文提出了一种新的基于互信息和遗传算法的监督、封装型特征选择算法.该算法设计了基于互信息的特征之间以及特征与类之间的相关性度量指标,并结合遗传算法具有的较强的全局寻优能力,在候选特征空间中寻找特征间相关性低,特征与类相关性高且分类精度高的全局最优特征子集.本文在10个标准数据集上,与8个基于相关性的特征选择算法进行了对比实验.在3个分类器下,本文算法对应的平均分类精度分别为88.98%,87.5%和86.95%,优于所有对比算法.结果表明,本文算法可以有效降低原始特征集的维数并提升分类器的精度.
Abstract
A novel feature selection algorithm using mutual information and genetic algorithm is presented in this paper.The algo-rithm designed the metrics for measuring the correlation between features and that between features and classes based on mutual in-formation.By combining the strong global optimization capability of genetic algorithms,it can search for a globally optimal feature subset in the candidate feature space,characterized by low inter-feature correlation,high feature-to-class correlation,and high classi-fication accuracy.In this paper,comparative experiments were conducted on 10 standard datasets using 8 correlation-based feature selection algorithms.Under 3 classifiers,the algorithm proposed in this paper achieves average classification accuracies of 88.98%,87.5%,and 86.95%,respectively,outperforming all the comparative algorithms.The experimental outcomes demonstrate the effec-tiveness of the proposed algorithm in significantly reducing the dimensionality of the original feature sets while enhancing the accu-racies of classifiers.
关键词
特征选择/相关性/熵/互信息/遗传算法Key words
feature selection/correlation/entropy/mutual information/genetic algorithm引用本文复制引用
基金项目
国家自然科学基金(62072291)
国家自然科学基金(62272284)
安徽省自然科学基金(2008085MF202)
出版年
2024
国家科技期刊平台
NSTL
万方数据