A deep model with local surrogate loss for general cost-sensitive multi-label learning
Journal
32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Pages
3239-3246
Date Issued
2018
Author(s)
Abstract
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. Copyright ? 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Subjects
Artificial intelligence; Deep learning; Iterative methods; Cost-sensitive; Cost-sensitive algorithm; Descent directions; Learning models; Local neighborhoods; Machine learning problem; Multi-label learning; Learning algorithms
Type
conference paper