Contents
Which is the best loss function for multi-label classification?
I read that for multi-class problems it is generally recommended to use softmax and categorical cross entropy as the loss function instead of mse and I understand more or less why. For my problem of multi-label it wouldn’t make sense to use softmax of course as each class probability should be independent from the other.
How is distribution balanced loss used in binary cross entropy loss?
The Distribution-Balanced Loss tackles these issues through two key modifications to the standard binary cross-entropy loss: 1) a new way to re-balance the weights that takes into account the impact caused by label co-occurrence, and 2) a negative tolerant regularization to mitigate the over-suppression of negative labels.
Is the KL divergence still inclined to give multi-label output?
UPDATE: By experiment, the modified KL-divergence is still inclined to give multi-class output rather than multi-label output. My experience with sigmoid cross-entropy was not very pleasant.
What’s the name of the new loss function?
In response to issues above, we propose a new loss function, called Distribution-Balanced Loss.
Can a neural network do multi label classification?
Neural network models can be configured to support multi-label classification and can perform well, depending on the specifics of the classification task. Multi-label classification can be supported directly by neural networks simply by specifying the number of target labels there is in the problem as the number of nodes in the output layer.
How to do multi label classification in Python?
Guide to multi-class multi-label classification with neural networks in python Often in machine learning tasks, you have multiple possible labels for one sample that are not mutually exclusive. This is called a multi-class, multi-label classification problem.
How are multiple labels used in machine learning?
Often in machine learning tasks, you have multiple possible labels for one sample that are not mutually exclusive. This is called a multi-class, multi-label classification problem. Obvious suspects are image classification and text classification, where a document can have multiple topics.