How to train a machine learning classification model when there are more than one correct label?

Question

I have a simple dataset with 20 features and 8 possible labels. For some of the records, however, there could be more than one correct label. I would like to train this model such that the predicted label is one of the possible labels. What would be a good way to accomplish this?

Example: Consider the following record:

[color: grey; legs:2; wings:2; mass: 120g;....]

Some records are labeled as "Sparrow" and few others are named as "Bird". During testing, I don't care which of these labels are assigned to the record as long as it is one of them.

Answer 1

This certainly depends on the model, but if you're using a neural network with cross-entropy loss, it is entirely possible.

With a constant increase in available data, there is an important need to organize it and modern classification problems often involve the prediction of multiple labels simultaneously associated with a single instance.

You can refer to the following link for more information regarding the same: https://towardsdatascience.com/journey-to-the-center-of-multi-label-classification-384c40229bff

For an in-depth tutorial on creating Deep Learning models for Multi-Label Classification, refer to the following link: https://nanonets.com/blog/multi-label-classification-using-deep-learning/