  Prediction of multi-dimensional labels plays an important role in machine
learning problems. We found that the classical binary labels could not reflect
the contents and their relationships in an instance. Hence, we propose a
multi-label classification model based on interval type-2 fuzzy logic. In the
proposed model, we use a deep neural network to predict the type-1 fuzzy
membership of an instance and another one to predict the fuzzifiers of the
membership to generate interval type-2 fuzzy memberships. We also propose a
loss function to measure the similarities between binary labels in datasets and
interval type-2 fuzzy memberships generated by our model. The experiments
validate that our approach outperforms baselines on multi-label classification
benchmarks.
