<p>You can try triplet learning instead of simple classification.</p>
<p><a href="https://i.stack.imgur.com/SOn0Y.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/SOn0Y.png" alt="triplet" /></a></p>
<p>From your 1000 users, you can make, c * 1000 * 999 / 2 pairs. c is the average number of samples per class/user.</p>
<p><a href="https://arxiv.org/pdf/1412.6622.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1412.6622.pdf</a></p>
Best Neural Network architecture for traditional large multiclass classification problem
<p>I am new to deep learning (I just finished to read <em>deep learning with pytorch</em>), and I was wondering what is the best neural network architecture for my case.</p>
<p>I have a large multiclass classification problem (user identification problem), about 1000 classes in which each class is a user. I have about 2000 features for each user after one-hot encoding and cleaning. Data are highly imbalanced, but I can always use oversampling/downsampling techniques.</p>
<p>I was wondering what is the best architecture to implement for my case. I've always seen deep learning applied to time series or images, so I'm not sure about what to use in this case. I was thinking about a multi-layer perceptron but maybe there are better solutions.</p>
<p>Thanks for your tips and help. Have a nice day!</p>