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Multi-label classification with feature-aware implicit encoding and generalized cross-entropy loss

Farahnak Ghazani, F ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/IranianCEE.2016.7585772
  3. Publisher: Institute of Electrical and Electronics Engineers Inc
  4. Abstract:
  5. In multi-label classification problems, each instance can simultaneously have multiple labels. Since the whole number of available labels in real-world applications tends to be (very) large, multi-label classification becomes an important challenge and recently label space dimension reduction (LSDR) methods have received attention. These methods first encode the output space to a low-dimensional latent space. Afterwards, they predict the latent space from the feature space and reconstruct the original output space using a suitable decoding method. The encoding method can be implicit which learns a code matrix in the latent space by solving an optimization problem or explicit which learns a direct encoding function. It can be feature-aware which considers predictability of the latent space from the feature space or not feature-aware which obtains the latent space from only the label space. In this paper, we propose FIECE as a feature-aware implicit encoding method that uses a generalized cross-entropy loss function to compute reconstruction error of the label space. Since label vectors in these problems are usually sparse, we use a parameter in the cost function to address the imbalanced classification problem for each label. Extensive experiments on several datasets demonstrate effectiveness of the proposed method compared to some recent multi-label classification methods
  6. Keywords:
  7. Feature-aware encoding ; Generalized cross-entropy ; Implicit encoding ; Label space dimension reduction ; Cost functions ; Encoding (symbols) ; Entropy ; Optimization ; Problem solving ; Cross entropy ; Decoding methods ; Encoding methods ; Imbalanced classification ; Label space ; Multi label classification ; Optimization problems ; Reconstruction error ; Classification (of information)
  8. Source: 24th Iranian Conference on Electrical Engineering, 10 May 2016 through 12 May 2016 ; 2016 , Pages 1574-1579 ; 9781467387897 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/7585772