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Multilabel classification outperforms detection-based technique

Author

Listed:
  • Gross, Ronit
  • Koresh, Ella
  • Halevi, Tal
  • Hodassman, Shiri
  • Meir, Yuval
  • Tzach, Yarden
  • Kanter, Ido

Abstract

In real-life scenarios, an input image typically comprises multiple objects, and their classification is often implemented using detection-based classification (DBC). In this approach, objects are first detected and then identified individually using a deep architecture. In this study, we demonstrate that the accuracy achieved by multilabel classification (MLC) surpasses that of DBC for a relatively small number of multilabel learning combinations. The crossover point at which DBC maximizes accuracy depends on the type of multilabel images, such as the number of multiple objects per image. The results are based on VGG-6 trained on the CIFAR-100 dataset using an upper bound for DBC accuracy, assumed under perfect detection conditions. Furthermore, we briefly discuss the potential relevance of these findings to advanced communication theory and natural language processing. The results suggest a need to reexamine the advantages of MLC over DBC using more complex datasets and deep architectures.

Suggested Citation

  • Gross, Ronit & Koresh, Ella & Halevi, Tal & Hodassman, Shiri & Meir, Yuval & Tzach, Yarden & Kanter, Ido, 2025. "Multilabel classification outperforms detection-based technique," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 658(C).
    • Handle: RePEc:eee:phsmap:v:658:y:2025:i:c:s0378437124008057
    DOI: 10.1016/j.physa.2024.130295
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    References listed on IDEAS

    as
    1. Koresh, Ella & Halevi, Tal & Meir, Yuval & Dilmoney, Dolev & Dror, Tamar & Gross, Ronit & Tevet, Ofek & Hodassman, Shiri & Kanter, Ido, 2024. "Scaling in Deep and Shallow Learning Architectures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 646(C).
    2. Meir, Yuval & Tevet, Ofek & Tzach, Yarden & Hodassman, Shiri & Kanter, Ido, 2024. "Role of delay in brain dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 654(C).
    3. Tevet, Ofek & Gross, Ronit D. & Hodassman, Shiri & Rogachevsky, Tal & Tzach, Yarden & Meir, Yuval & Kanter, Ido, 2024. "Efficient shallow learning mechanism as an alternative to deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    4. Grigorios Tsoumakas & Ioannis Katakis, 2007. "Multi-Label Classification: An Overview," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 3(3), pages 1-13, July.
    5. Meir, Yuval & Tevet, Ofek & Koresh, Ella & Tzach, Yarden & Kanter, Ido, 2024. "Advanced confidence methods in deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
    Full references (including those not matched with items on IDEAS)

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