TY - JOUR AU - Hsieh, Cheng-Yu AU - Lin, Yi-An AU - Lin, Hsuan-Tien PY - 2018/04/29 Y2 - 2024/03/29 TI - A Deep Model With Local Surrogate Loss for General Cost-Sensitive Multi-Label Learning JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 32 IS - 1 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v32i1.11816 UR - https://ojs.aaai.org/index.php/AAAI/article/view/11816 SP - AB - <p> 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. </p> ER -