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Binary-Tree Based Mean-Averaging Estimation for Multi-label Classification

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Pattern Recognition (ICPR 2024)

Abstract

Multi-label classification(MLC) is a machine learning problem where each instance may belong to more than one class at the same time. Due to overlapping classes and label-label correlation, solving MLC is very challenging. Further, class imbalance and computational time-complexity are also considered to be major issues. In this paper, we have proposed a novel multi-label classifier that addressed the aforementioned issues; termed as Binary-Tree based Mean-Averaging estimation for Multi-label classification (BT-MA (Code is available at: https://github.com/ml-lab-sau/BT-MA).). This proposed classifier takes distinct label-sets meta-feature into account for recovering data imbalance and employs the Divide-and-conquer strategy for resolving time-complexity issue. The experimental results on several benchmark data sets show that our proposed approach BT-MA is as competitive as other Multi-label classification approaches.

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Notes

  1. 1.

    Yahoo-Arts Dataset.

  2. 2.

    Bookmarks Dataset.

  3. 3.

    Yahoo-Science Dataset.

  4. 4.

    Bibtex Dataset.

  5. 5.

    GnegativeGO Dataset.

  6. 6.

    PlantGO Dataset.

  7. 7.

    BR-SVM model’s code from MLC Toolbox.

  8. 8.

    ML-KNN model’s code from PALM Lab.

  9. 9.

    LSML model’s code from Huang Jun’s Site.

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Correspondence to Reshma Rastogi .

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Rastogi, R., Chowdhury, S. (2025). Binary-Tree Based Mean-Averaging Estimation for Multi-label Classification. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15310. Springer, Cham. https://doi.org/10.1007/978-3-031-78192-6_18

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  • DOI: https://doi.org/10.1007/978-3-031-78192-6_18

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