Hierarchical Feature Selection from Coarse to Fine

LIU Hao-yang; LIN Yao-jin; LIU Jing-hua; WU Yi-lin; MAO Yu; LI Shao-zi

doi:10.12263/DZXB.20211263

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Hierarchical Feature Selection from Coarse to Fine

PAPERS | 更新时间：2025-12-08

- Hierarchical Feature Selection from Coarse to Fine
- ACTA ELECTRONICA SINICA Vol. 50, Issue 11, Pages: 2778-2789(2022)
- 作者机构：
  
  1.闽南师范大学计算机学院, 福建漳州 363000
  2.数据科学与智能应用福建省高等学校重点实验室(闽南师范大学), 福建漳州 363000
  3.华侨大学计算机科学与技术学院, 福建厦门 361021
  4.厦门大学人工智能系, 福建厦门 361005
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20211263
  CLC： TP181
- Received：14 September 2021，
  
  Revised：2021-11-02，
  
  Published：25 November 2022
- 稿件说明：
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刘浩阳,林耀进,刘景华等.由粗到细的分层特征选择[J].电子学报,2022,50(11):2778-2789.

LIU Hao-yang,LIN Yao-jin,LIU Jing-hua,et al.Hierarchical Feature Selection from Coarse to Fine[J].ACTA ELECTRONICA SINICA,2022,50(11):2778-2789.
刘浩阳,林耀进,刘景华等.由粗到细的分层特征选择[J].电子学报,2022,50(11):2778-2789. DOI： 10.12263/DZXB.20211263.

LIU Hao-yang,LIN Yao-jin,LIU Jing-hua,et al.Hierarchical Feature Selection from Coarse to Fine[J].ACTA ELECTRONICA SINICA,2022,50(11):2778-2789. DOI： 10.12263/DZXB.20211263.

摘要

利用数据类别间层次结构关系进行分类学习任务广泛存在于疾病诊断、图像标注等领域.然而

数据特征空间的高维性

使得分层分类学习面临着时间复杂度高和存储负担大等问题.另外

现有研究工作都假设训练集标记粒度是充分细化

与实际分层分类学习中划分细粒度标记代价高

类别标记间存在语义歧义性等矛盾.为解决上述问题

提出一种由粗到细的分层特征选择算法.该算法考虑类内一致性和兄弟节点间的差异性以选择有代表性特征

同时在特征选择的过程中实现预测训练样本未知的细粒度标记.在7个基准数据集上的实验结果表明

所提算法的分类性能优于一些先进的对比算法

且能处理标记粒度不够细化的情况.

Abstract

The task of classification learning using hierarchy of categories in data exists widely in many practical applications such as disease diagnosis

image annotation

etc. However

the high dimensionality of data feature space makes hierarchical classification learning confront problems such as high time and space complexity. In addition

existing research works assume that the training set label granularity is sufficiently fine-grained

which is contradictory to the actual hierarchical classification learning

i.e.

dividing fine-grained labels is costly and ambiguity exists among category labels. To solve the above problems

we propose a coarse-to-fine hierarchical feature selection algorithm. We consider intra-class consistency and inter-sibling variability to select representative features and the unknown fine-grained labels of the training samples are predicted during feature selection. Experimental results on seven benchmark datasets show that the proposed algorithm outperforms some advanced comparative algorithms in classification performance and can handle the case where the label granularity is not fine-grained enough.

关键词

Keywords

references

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Related Institution

School of Computer Science， Minnan Normal University

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