A Deep Model With Local Surrogate Loss for General Cost-Sensitive Multi-Label Learning


  • Cheng-Yu Hsieh National Taiwan University
  • Yi-An Lin National Taiwan University
  • Hsuan-Tien Lin National Taiwan University


Multi-label learning is an important machine learning problem with a wide range of applications. The variety of criteria for satisfying different application needs calls for cost-sensitive algorithms, which can adapt to different criteria easily. Nevertheless, because of the sophisticated nature of the criteria for multi-label learning, cost-sensitive algorithms for general criteria are hard to design, and current cost-sensitive algorithms can at most deal with some special types of criteria. In this work, we propose a novel cost-sensitive multi-label learning model for any general criteria. Our key idea within the model is to iteratively estimate a surrogate loss that approximates the sophisticated criterion of interest near some local neighborhood, and use the estimate to decide a descent direction for optimization. The key idea is then coupled with deep learning to form our proposed model. Experimental results validate that our proposed model is superior to existing cost-sensitive algorithms and existing deep learning models across different criteria.




How to Cite

Hsieh, C.-Y., Lin, Y.-A., & Lin, H.-T. (2018). A Deep Model With Local Surrogate Loss for General Cost-Sensitive Multi-Label Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11816