计算机科学
特征选择
人口
人工智能
特征(语言学)
机器学习
等级制度
选择(遗传算法)
预处理器
数据挖掘
模式识别(心理学)
萤火虫算法
粒子群优化
语言学
哲学
人口学
社会学
经济
市场经济
作者
Jia Zhao,Siyu Lv,Renbin Xiao,Huan Ma,Jeng‐Shyang Pan
标识
DOI:10.1016/j.asoc.2024.112042
摘要
Feature selection is a crucial data preprocessing technique extensively employed in machine learning and image processing. However, feature selection encounters significant challenges when addressing high-dimensional data due to the huge and discrete decision space. This paper proposes a hierarchical learning multi-objective firefly algorithm (HMOFA) for solving the feature selection task in high-dimensional data. The main contributions are as follows: 1) Features are clustered based on the evaluation of multiple metrics, which are used to initialize the population and improve the quality of the initial population; 2) A hierarchy-guided learning model is proposed, where individuals move toward superior solutions while moving away from inferior solutions, avoiding the oscillation phenomenon that occurs under the full attraction model, and reducing the likelihood of the population being trapped in a local optimum; 3) Use duplicate solution modification mechanism to reduce the number of duplicate individuals in the population. The proposed method is compared with 8 competitive feature selection methods using 15 datasets, and the results demonstrate that HMOFA can achieve higher classification accuracy while selecting fewer features.
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