research-article

An Overview of Label Space Dimension Reduction for Multi-Label Classification

Authors:

Jianhou GanAuthors Info & Claims

ICIIP '17: Proceedings of the 2nd International Conference on Intelligent Information Processing

Article No.: 14, Pages 1 - 6

https://doi.org/10.1145/3144789.3144807

Published: 17 July 2017 Publication History

Abstract

Multi-label classification with many labels are common in real-world application. However, traditional multi-label classifiers often become computationally inefficient for hundreds or even thousands of labels. Therefore, the label space dimension reduction is designed to address this problem. In this paper, the existing studies of label space dimension reduction are summarized; especially, these studies were classified into two categories: label space dimension reduction based on transformed labels and label subset; meanwhile, we analyze the studies belonging to each type and give the experimental comparison of two typical LSDR algorithms. To the best of our knowledge, this is the first effort to review the development of label space dimension reduction.

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Cited By

Ebrahimi S(2024)A Hybrid Principal Label Space Transformation-Based Binary Relevance Support Vector Machine and Q-Learning Algorithm for Multi-label ClassificationArabian Journal for Science and Engineering10.1007/s13369-024-09034-1Online publication date: 20-Apr-2024
https://doi.org/10.1007/s13369-024-09034-1

Index Terms

An Overview of Label Space Dimension Reduction for Multi-Label Classification
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees

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cover image ACM Other conferences

ICIIP '17: Proceedings of the 2nd International Conference on Intelligent Information Processing

July 2017

211 pages

ISBN:9781450352871

DOI:10.1145/3144789

Copyright © 2017 ACM.

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Wanfang Data: Wanfang Data, Beijing, China
International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 July 2017

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IIP'17

IIP'17: 2017 2nd International Conference on Intelligent Information Processing

July 17 - 18, 2017

Bangkok, Thailand

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ICIIP '17 Paper Acceptance Rate 32 of 202 submissions, 16%;

Overall Acceptance Rate 87 of 367 submissions, 24%

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Cited By

Ebrahimi S(2024)A Hybrid Principal Label Space Transformation-Based Binary Relevance Support Vector Machine and Q-Learning Algorithm for Multi-label ClassificationArabian Journal for Science and Engineering10.1007/s13369-024-09034-1Online publication date: 20-Apr-2024
https://doi.org/10.1007/s13369-024-09034-1

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