Interval type-2 fuzzy neural networks for multi-label classification

Abstract

Prediction of multi-dimensional labels plays an important role in machine learning problems. We discovered that traditional binary labels could not capture the contents and 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 an instance’s type-1 fuzzy membership and another to predict the membership’s fuzzifiers, resulting in interval type-2 fuzzy memberships. We also propose a loss function for determining 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 in multi-label classification benchmarks.