Joint subspace reconstruction and label correlation for multi-label feature selection-Reference-Cited by-同舟云学术

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Joint subspace reconstruction and label correlation for multi-label feature selection

Published:2023-12-29 Issue:1 Volume:54 Page:1117-1143
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Wang Zelong,Chen Hongmei,Mi Yong,Luo Chuan,Horng Shi-Jinn,Li Tianrui

Funder

National Natural Science Foundation of China

Natural Science Foundation of Sichuan Province

Key Research and Development Program of Sichuan Province

Sichuan Science and Technology Achievements Transfer and Transformation Demonstration Project

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-023-05188-x.pdf

Reference54 articles.

1. Cai Z, Zhu W (2018) Multi-label feature selection via feature manifold learning and sparsity regularization. Int J Mach Learn Cybern 9:1–14

2. Chauhan S, Singh M, Aggarwal AK (2021) Experimental analysis of effect of tuning parameters on the performance of diversity-driven multi-parent evolutionary algorithm. In: 2021 IEEE 2Nd international conference on electrical power and energy systems (ICEPES), pp 1–6

3. Chauhan S, Singh M, Aggarwal AK (2023) Designing of optimal digital IIR filter in the multi-objective framework using an evolutionary algorithm. Eng Appl Artif Intell 119:105803

4. Chauhan S, Singh M, Aggarwal AK (2023b) Investigative analysis of different mutation on diversity-driven multi-parent evolutionary algorithm and its application in area coverage optimization of WSN. Soft Computing pp 1–27

5. Chen SB, Zhang YM, Ding CH, Zhang J, Luo B (2019) Extended adaptive lasso for multi-class and multi-label feature selection. Knowl-Based Syst 173:28–36

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