Interval Type-2 Fuzzy Neural Networks for Multi-Label Classification
This addresses multi-label classification for machine learning applications, but appears incremental as it builds on existing fuzzy logic and neural network methods.
The paper tackles the problem of multi-label classification by proposing a model based on interval type-2 fuzzy logic, which outperforms baselines on benchmarks.
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.