|Table of Contents|

Online Hierarchical Streaming Feature Selection Based on Feature Interaction(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2024年02期
Page:
34-42
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Online Hierarchical Streaming Feature Selection Based on Feature Interaction
Author(s):
Kong Lingwei1; 2; Cai Linsheng1; 2; Lin Shaojie1; 2; Lin Yaojin1; 2
(1.School of Computer Science,Minnan Normal University,Zhangzhou 363000,China)
(2.Fujian Key Laboratory of Data Science and Intelligence Application,Minnan Normal University,Zhangzhou 363000,China)
Keywords:
online streaming feature selection; hierarchical classification; feature interaction; sibling strategy; neighborhood rough set
PACS:
TP181
DOI:
10.3969/j.issn.1672-1292.2024.02.005
Abstract:
In classification learning tasks in open dynamic environments,the data feature space is dynamic and there is a hierarchical structure in the labelling space. Existing hierarchical classification online streaming feature selection algorithms can select a superior subset of features,but these algorithms ignore the interactions that exist between the features. Therefore,this paper proposes a feature selection algorithm for hierarchical classification online streaming based on feature interaction. Firstly,a computational method based on hierarchical neighborhood dependency is designed to judge the feature interaction. Secondly,for hierarchical structure data,a neighborhood rough set model is defined on the basis of sibling relationships between different nodes in the hierarchical structure. Finally,the online streaming framework is designed for hierarchical classification with online importance analysis,online redundancy analysis and online interaction analysis for selecting the subset of features that are strongly correlated and have interaction. The proposed algorithm is experimentally verified on six hierarchical datasets to have superior comprehensive performance.

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Last Update: 2024-06-15