|Table of Contents|

Online Hierarchical Streaming Feature Selection Based on Neighborhood Decision Error Rate(PDF)

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

Issue:
2022年04期
Page:
9-18
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Online Hierarchical Streaming Feature Selection Based on Neighborhood Decision Error Rate
Author(s):
Wang Chenxi1; 2; Liu Yuankui1; 2; Lv Yan1; 2; Lin Yaojin1; 2
(1.School of Computer Science,Minnan Normal University,Zhangzhou 363000,China)
(2.Key Laboratory of Data Science and Intelligence Application,Minnan Normal University,Zhangzhou 363000,China)
Keywords:
online streaming feature selection; hierarchical classification; sibling relationships; neighborhood decision error rate
PACS:
TP18
DOI:
10.3969/j.issn.1672-1292.2022.04.002
Abstract:
In many practical application fields,there are numerous scenes in which the entire feature space cannot be available in advance,candidate features flow into the feature space dynamically over time,and the number of samples is fixed. At the same time,there exists a hierarchical structure relationship between classes,and traditional feature selection methods cannot be able to meet the demand. Based on these,an online streaming feature selection algorithm for hierarchical classification learning is presented. Firstly,a decision error rate calculation formula is designed on the basis of the largest nearest neighbor according to sibling relationships. Secondly,two online evaluation criteria of online significance selection and online relevance analysis are proposed to select features with minimum decision error. Finally,experimental results on six hierarchical datasets manifest that the proposed algorithm is better than some existing online streaming feature selection algorithms.

References:

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Last Update: 2022-12-15