Objective function for multi-label classification The customary objective function for multi-label (e.g. M labels) classification is binary cross-entropy. The problem is, if we use binary cross-entropy, we are assuming that the output labels are independent of each other, turning the problem to M independent binary classification problems. Is there any suitable objective function that makes the output labels to be dependent on each other?
What you're looking for is called "cost-sensitive classification". Most methods however don't work with label similarities, but rather with relative penalties for different types of misclassifications.