Answers.Body: <p><strong>What hierarchical loss functions are worth trying</strong>
  There are a number of hierarchical loss functions that have been described (see  articles <a href="https://link.springer.com/article/10.1007/s10618-010-0175-9" rel="nofollow noreferrer">#1</a> and <a href="https://Making%20Better%20Mistakes:%20Leveraging%20Class%20Hierarchies%20with%20Deep%20Networks" rel="nofollow noreferrer">#2</a>), such as <a href="https://arxiv.org/abs/1709.01062#:%7E:text=A%20hierarchical%20loss%20and%20its%20problems%20when%20classifying%20non%2Dhierarchically,-Cinna%20Wu%2C%20Mark&amp;text=Failing%20to%20distinguish%20between%20a,are%20both%20breeds%20of%20dogs." rel="nofollow noreferrer">hierarchical win</a> and <a href="https://ieeexplore.ieee.org/document/6247938" rel="nofollow noreferrer">normalized LCA</a>.  The loss functions that you noted will not by themselves leverage the hierarchical information.</p>
  
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Answers.CreationDate: 2022-10-21T02:43:30.583Z
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Body: <p>I am looking to try different loss functions for a hierarchical multi-label classification problem. So far, I have been training different models or submodels like multilayer perceptron (MLP) branch inside a bigger model which deals with different levels of classification, yielding a binary vector. I have been also using Binary Cross-Entropy (BCE) and summing all the losses existing in the model before backpropagating.</p>
  <p>I am considering trying other losses like MultiLabelSoftMarginLoss and MultiLabelMarginLoss.</p>
  <p>What other loss functions are worth trying? Hamming loss perhaps or a variation? Is it better to sum all the losses and backpropagate or do multiple backpropagations?</p>
  
Tags: <neural-networks><deep-learning><objective-functions><multi-label-classification>
Title: Which other loss functions for hierarchical multi-label classification could I use?
UserDisplayName: Skinish
ThreadText: # Which other loss functions for hierarchical multi-label classification could I use?
  
  (Asked by: username_0 on 2018-08-13)
  
  I am looking to try different loss functions for a hierarchical multi-label classification problem. So far, I have been training different models or submodels like multilayer perceptron (MLP) branch inside a bigger model which deals with different levels of classification, yielding a binary vector. I have been also using Binary Cross-Entropy (BCE) and summing all the losses existing in the model before backpropagating.
  
  I am considering trying other losses like MultiLabelSoftMarginLoss and MultiLabelMarginLoss.
  
  What other loss functions are worth trying? Hamming loss perhaps or a variation? Is it better to sum all the losses and backpropagate or do multiple backpropagations?
  
  **Tags:** neural-networks, deep-learning, objective-functions, multi-label-classification
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2022-10-21. Score: 0
  
  **What hierarchical loss functions are worth trying**
  There are a number of hierarchical loss functions that have been described (see articles [#1](https://link.springer.com/article/10.1007/s10618-010-0175-9) and [#2](https://Making%20Better%20Mistakes:%20Leveraging%20Class%20Hierarchies%20with%20Deep%20Networks)), such as [hierarchical win](https://arxiv.org/abs/1709.01062#:%7E:text=A%20hierarchical%20loss%20and%20its%20problems%20when%20classifying%20non%2Dhierarchically,-Cinna%20Wu%2C%20Mark&text=Failing%20to%20distinguish%20between%20a,are%20both%20breeds%20of%20dogs.) and [normalized LCA](https://ieeexplore.ieee.org/document/6247938). The loss functions that you noted will not by themselves leverage the hierarchical information.
  
  
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