# Interval Type-2 Fuzzy Neural Networks for Multi-Label Classification

arXiv: https://arxiv.org/abs/2302.10430 · HTML: https://mlsanity.com/paper/2302.10430/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 56 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 20 of 20)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
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- [Multi-Label Image Recognition with Graph Convolutional Networks](https://mlsanity.com/paper/1904.03582/) (2019)
- [Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders](https://mlsanity.com/paper/1611.02648/) (2016)
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- [CNN-RNN: A Unified Framework for Multi-label Image Classification](https://mlsanity.com/paper/1604.04573/) (2016)
- [Deep Set Prediction Networks](https://mlsanity.com/paper/1906.06565/) (2019)
- [Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures](https://mlsanity.com/paper/1805.06627/) (2018)
- [Structured Prediction Energy Networks](https://mlsanity.com/paper/1511.06350/) (2015)
- [End-to-End Learning for Structured Prediction Energy Networks](https://mlsanity.com/paper/1703.05667/) (2017)
- [Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model](https://mlsanity.com/paper/2007.06126/) (2020)
- [Learning Deep Latent Spaces for Multi-Label Classification](https://mlsanity.com/paper/1707.00418/) (2017)
- [Neural Message Passing for Multi-Label Classification](https://mlsanity.com/paper/1904.08049/) (2019)
- [Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of β-VAE](https://mlsanity.com/paper/2003.08740/) (2020)
- [Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs](https://mlsanity.com/paper/1703.04363/) (2017)
- [Improving Joint Training of Inference Networks and Structured Prediction Energy Networks](https://mlsanity.com/paper/1911.02891/) (2019)
- [Predict and Constrain: Modeling Cardinality in Deep Structured Prediction](https://mlsanity.com/paper/1802.04721/) (2018)
- [Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms](https://mlsanity.com/paper/2206.04311/) (2022)

## Abstract (arXiv)

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.