Skip to main content
SpringerLink
Log in

Parallel dual-channel multi-label feature selection

  • Data analytics and machine learning
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In the process of multi-label learning, feature selection methods are often adopted to solve the high-dimensionality problem in feature spaces. Most existing multi-label feature selection algorithms focus on exploring the correlation between features and labels and then obtain the target feature subset by importance ranking. These algorithms commonly use single-channel structure to obtain important features, which induces the excessive reliance on the ranking results and causes the loss of important features. However, the correlation between label-specific feature and label-instance is ignored. Therefore, this paper proposes Parallel Dual-channel Multi-label Feature Selection algorithm (PDMFS). We first introduce the concept of dual channel and design the algorithm model as two independent modules. The algorithm obtained different feature correlation sequences, thus avoided relevant feature loss. And then, the proposed algorithm uses the subspace model to select the feature subset with the maximum correlation and minimum redundancy for each sequence, thus obtaining feature subsets under respective correlations. Finally, the subsets are cross-merged to reduce the important feature loss caused by the serial structure processing single feature correlation. The experimental results on eight datasets and statistical hypothesis testing indicate that the proposed algorithm is effective.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

All datasets are publicly downloadable from internet. The specific URLs are given in Sect. 5.

References

  • Aim H, Mohammad DB, Hossein N (2021) An efficient Pareto-based feature selection algorithm for multi-label classification. Inf Sci 581:428–447

    Article  MathSciNet  Google Scholar 

  • Amin H, Mohammad BD, Hossein N (2020) MFS-MCDM: Multi-label feature selection using multi-criteria decision making. Knowl-Based Syst 206:106365

    Article  Google Scholar 

  • Cheng YS, Zhang C, Pang SF (2022) Multi-label space reshape for semantic-rich label-specific features learning. Int J Mach Learn Cybern 13(4):1005–1019

    Article  Google Scholar 

  • Cui X, Zou C, Wang Z (2021) Remote sensing image recognition based on dual-channel deep learning network. Multimedia Tools Appl 80(18):27683–27699

    Article  Google Scholar 

  • Estrela G, Gubitoso MD, Ferreira CE et al (2020) An efficient, parallelized algorithm for optimal conditional entropy-based feature selection. Entropy 22(4):492

    Article  MathSciNet  Google Scholar 

  • Fan Y, Liu J, Weng W et al (2021a) Multi-label feature selection with constraint regression and adaptive spectral graph. Knowl-Based Syst 212:106621

    Article  Google Scholar 

  • Fan Y, Liu J, Weng W et al (2021b) Multi-label feature selection with local discriminant model and label correlations. Neurocomputing 442:98–115

    Article  Google Scholar 

  • Guo X, Yu K, Liu L et al (2022) Causal feature selection with dual correction. IEEE Trans Neural Networks Learn Syst. https://doi.org/10.1109/TNNLS.2022.3178075

    Article  Google Scholar 

  • Hu L, Li Y, Gao W et al (2020) Multi-label feature selection with shared common mode. Pattern Recogn 104:107344

    Article  Google Scholar 

  • Huang J, Li G, Huang Q, et al (2015) Learning label specific features for multi-label classification. In: Proceedings of the IEEE international conference on data mining. IEEE, Atlantic City, New Jersey, USA pp 181–190

  • Huang R, Wu Z (2021) Multi-label feature selection via manifold regularization and dependence maximization. Pattern Recogn 120:108149

    Article  Google Scholar 

  • Janez D, Dale S (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

  • Jiang L, Yu G, Guo M et al (2020) Feature selection with missing labels based on label compression and local feature correlation. Neurocomputing 395:95–106

    Article  Google Scholar 

  • Lee J, Kim DW (2013) Feature selection for multi-label classification using multivariate mutual information. Pattern Recogn Lett 34(3):349–357

    Article  Google Scholar 

  • Lee J, Kim DW (2015a) Memetic feature selection algorithm for multi-label classification. Inf Sci 293:80–96

    Article  Google Scholar 

  • Lee J, Kim DW (2015b) Mutual information based multi-label feature selection using interaction information. Expert Syst Appl 42(4):2013–2025

    Article  Google Scholar 

  • Li Y, Cheng Y (2019) Streaming feature selection for multi-label data with dynamic sliding windows and feature repulsion loss. Entropy 21(12):1151

    Article  MathSciNet  Google Scholar 

  • Li H, Zheng Y, Ren P (2019) Dual-channel attention model for text sentiment analysis. Int J Perform Eng 15(3):834–841

    Google Scholar 

  • Lin Y, Hu X, Wu X (2014) Quality of information-based source assessment and selection. Neurocomputing 133:95–102

    Article  Google Scholar 

  • Lin Y, Hu Q, Liu J (2015) Multi-label feature selection based on max-dependency and min-redundancy. Neuro Comput 168:92–103

    Google Scholar 

  • Lin Y, Hu Q, Liu J et al (2016) Multi-label feature selection based on neighborhood mutual information. Appl Soft Comput 38:244–256

    Article  Google Scholar 

  • Liu J, Lin M, Wang C et al (2016) Multi-label feature selection algorithm based on local subspace. Pattern Recognit Artif Intell 29(3):240–251

    Google Scholar 

  • Schapire RE, Singer Y (2000) BoosTexter: a boosting-based system for text categorization. Mach Learn 39(2):135–168

    Article  MATH  Google Scholar 

  • Spolaôr N, Cherman EA, Monard MC et al (2013) A comparison of multi-label feature selection methods using the problem transformation approach. Electron Notes Theor Comput Sci 292:135–151

    Article  Google Scholar 

  • Wang Y, Zheng W, Cheng Y et al (2020) Joint label completion and label-specific features for multi-label learning algorithm. Soft Comput 24(9):6553–6569

    Article  Google Scholar 

  • Wang Y, Zheng W, Cheng Y et al (2021a) Two-level label recovery-based label embedding for multi-label classification with missing labels. Appl Soft Comput 99:106868

    Article  Google Scholar 

  • Wang X, Liu Y, Du Z et al (2021b) Prediction of protein solubility based on sequence feature fusion and DDcCNN. Interdiscip Sci Comput Life Sci 13(4):703–716

    Article  Google Scholar 

  • Wu X, Jiang B, Yu K, et al (2020) Multi-label causal feature selection. In: Proceedings of the AAAI conference on artificial intelligence 34(04): 6430–6437

  • Xu Y, Lu L, Xu Z et al (2019) Dual-channel CNN for efficient abnormal behavior identification through crowd feature engineering. Mach vis Appl 30(5):945–958

    Article  Google Scholar 

  • Yu K, Cai M, Wu X et al (2021) Multilabel feature selection: a local causal structure learning approach. IEEE Trans Neural Networks Learn Syst. https://doi.org/10.1109/TNNLS.2021.3111288

    Article  Google Scholar 

  • Zeng Z, Wang X, Chen Y (2017) Multimedia annotation via semi-supervised shared-subspace feature selection. J vis Commun Image Represent 48:386–395

    Article  Google Scholar 

  • Zhang ML, Zhou ZH (2007) ML-KNN: A lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048

    Article  MATH  Google Scholar 

  • Zhang Y, Zhou ZH (2010) Multilabel dimensionality reduction via dependence maximization. ACM Trans Knowl Discov Data 4(3):1–21

    Article  Google Scholar 

  • Zhang ML, Zhou ZH (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837

    Article  Google Scholar 

  • Zhang P, Gao W, Hu J et al (2020) Multi-label feature selection based on high-order label correlation assumption. Entropy 22(7):797

    Article  MathSciNet  Google Scholar 

  • Zhang P, Liu G, Gao W et al (2021a) Multi-label feature selection considering label supplementation. Pattern Recogn 120:108137

    Article  Google Scholar 

  • Zhang L, Cheng T, Wang Y et al (2021b) Feature-label dual-mapping for missing label-specific features learning. Soft Comput 25(14):9307–9323

    Article  Google Scholar 

  • Zhang J, Lin Y, Jiang M et al (2022) Fast multilabel feature selection via global relevance and redundancy optimization. IEEE Trans Neural Networks Learn Syst. https://doi.org/10.1109/TNNLS.2022.3208956

    Article  Google Scholar 

  • Zhang J, Wu H, Jiang M et al (2023) Group-preserving label-specific feature selection for multi-label learning. Expert Syst Appl 213:118861

    Article  Google Scholar 

  • Zhang L, Hu Q, Duan J, et al (2014) Multi-label feature selection with fuzzy rough sets. In: International conference on rough sets and knowledge technology; Springer International Publishing: Cham, Switzerland, pp 121–128

  • Zhou F, Ma Y, Wang B et al (2021) Dual-channel convolutional neural network for power edge image recognition. J Cloud Comput 10(1):1–9

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of Anhui under Grant 2108085MF216; the Key Laboratory of Data Science and Intelligence Application, Fujian Province University (NO. D202005); and the Graduate Academic Innovation Program of Anqing Normal University.

Funding

This work was supported by the National Natural Science Foundation of Anhui (2108085MF216) and the Key Laboratory of Data Science and Intelligence Application, Fujian Province University (D202005).

Author information

Authors and Affiliations

  1. School of Computer and Information, Anqing Normal University, Anqing, 246011, Anhui, China

    Jiali Miao, Yibin Wang & Yusheng Cheng

  2. The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing, 246133, Anhui, China

    Yibin Wang & Yusheng Cheng

  3. School of Mathematics and Computer, Tongling University, Tongling, 244061, Anhui, China

    Fei Chen

Authors
  1. Jiali Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yibin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yusheng Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yusheng Cheng.

Ethics declarations

Conflict of interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Miao, J., Wang, Y., Cheng, Y. et al. Parallel dual-channel multi-label feature selection. Soft Comput 27, 7115–7130 (2023). https://doi.org/10.1007/s00500-023-07916-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-07916-4

Keywords

Search

Navigation