马尔可夫毯
计算机科学
架空(工程)
编码(内存)
马尔可夫链
特征(语言学)
特征选择
维数(图论)
模式识别(心理学)
遗传算法
算法
数据挖掘
选择(遗传算法)
人工智能
马尔可夫模型
机器学习
数学
变阶马尔可夫模型
操作系统
哲学
纯数学
语言学
作者
Junhai Zhou,Quanwang Wu,MengChu Zhou,Junhao Wen,Yusuf Al‐Turki,Abdullah Abusorrah
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2023-11-01
卷期号:53 (11): 6858-6869
被引量:4
标识
DOI:10.1109/tcyb.2022.3163577
摘要
Feature selection (FS) is an essential technique widely applied in data mining. Recent studies have shown that evolutionary computing (EC) is very promising for FS due to its powerful search capability. However, most existing EC-based FS methods use a length-fixed encoding to represent feature subsets. This inflexible encoding turns ineffective when high-dimension data are handled, because it results in a huge search space, as well as a large amount of training time and memory overhead. In this article, we propose a length-adaptive genetic algorithm with Markov blanket (LAGAM), which adopts a length-variable individual encoding and enables individuals to evolve in their own search space. In LAGAM, features are rearranged decreasingly based on their relevance, and an adaptive length changing operator is introduced, which extends or shortens an individual to guide it to explore in a better search space. Local search based on Markov blanket (MB) is embedded to further improve individuals. Experiments are conducted on 12 high-dimensional datasets and results reveal that LAGAM performs better than existing methods. Specifically, it achieves a higher classification accuracy by using fewer features.
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