Multi-label Feature Selection via Global Relevance and Redundancy Optimization

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

6 Scopus Citations
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Author(s)

  • Jia Zhang
  • Yidong Lin
  • Min Jiang
  • Shaozi Li
  • Yong Tang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20)
EditorsChristian Bessiere
PublisherInternational Joint Conferences on Artificial Intelligence
Pages2512-2518
ISBN (Electronic)9780999241165
Publication statusPublished - Jan 2021

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Title29th International Joint Conference on Artificial Intelligence (IJCAI 2020)
LocationVirtual
PlaceJapan
CityYokohama
Period7 - 15 January 2021

Abstract

Information theoretical based methods have attracted a great attention in recent years, and gained promising results to deal with multi-label data with high dimensionality. However, most of the existing methods are either directly transformed from heuristic single-label feature selection methods or inefficient in exploiting labeling information. Thus, they may not be able to get an optimal feature selection result shared by multiple labels. In this paper, we propose a general global optimization framework, in which feature relevance, label relevance (i.e., label correlation), and feature redundancy are taken into account, thus facilitating multi-label feature selection. Moreover, the proposed method has an excellent mechanism for utilizing inherent properties of multi-label learning. Specially, we provide a formulation to extend the proposed method with label-specific features. Empirical studies on twenty multi-label data sets reveal the effectiveness and efficiency of the proposed method. Our implementation of the proposed method is available online at: https://jiazhang-ml.pub/GRRO-master.zip.

Citation Format(s)

Multi-label Feature Selection via Global Relevance and Redundancy Optimization. / Zhang, Jia; Lin, Yidong; Jiang, Min; Li, Shaozi; Tang, Yong; Tan, Kay Chen.

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20). ed. / Christian Bessiere. International Joint Conferences on Artificial Intelligence, 2021. p. 2512-2518 (IJCAI International Joint Conference on Artificial Intelligence).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review