Hsieh C.-YLin Y.-AHSUAN-TIEN LIN2021-09-022021-09-022018https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060473521&partnerID=40&md5=246dded8cac1fad7b94b11a2ba8eb95bhttps://scholars.lib.ntu.edu.tw/handle/123456789/581368Multi-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. Copyright ? 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.Artificial intelligence; Deep learning; Iterative methods; Cost-sensitive; Cost-sensitive algorithm; Descent directions; Learning models; Local neighborhoods; Machine learning problem; Multi-label learning; Learning algorithmsconference paper2-s2.0-85060473521