# Multi-Label Takagi-Sugeno-Kang Fuzzy System

arXiv: https://arxiv.org/abs/2309.11469 · HTML: https://mlsanity.com/paper/2309.11469/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 54 references that are in the map.
- First posted on arXiv 20 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [Learning Deep Latent Spaces for Multi-Label Classification](https://mlsanity.com/paper/1707.00418/) (2017)
- [Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification](https://mlsanity.com/paper/1802.00891/) (2018)
- [Reconstruction Regularized Deep Metric Learning for Multi-label Image Classification](https://mlsanity.com/paper/2007.13547/) (2020)
- [Multi-View Fuzzy Logic System with the Cooperation between Visible and Hidden Views](https://mlsanity.com/paper/1807.08595/) (2018)

## Abstract (arXiv)

Multi-label classification can effectively identify the relevant labels of an instance from a given set of labels. However,the modeling of the relationship between the features and the labels is critical to the classification performance. To this end, we propose a new multi-label classification method, called Multi-Label Takagi-Sugeno-Kang Fuzzy System (ML-TSK FS), to improve the classification performance. The structure of ML-TSK FS is designed using fuzzy rules to model the relationship between features and labels. The fuzzy system is trained by integrating fuzzy inference based multi-label correlation learning with multi-label regression loss. The proposed ML-TSK FS is evaluated experimentally on 12 benchmark multi-label datasets. 1 The results show that the performance of ML-TSK FS is competitive with existing methods in terms of various evaluation metrics, indicating that it is able to model the feature-label relationship effectively using fuzzy inference rules and enhances the classification performance.