# A Robust Multilabel Method Integrating Rule-based Transparent Model, Soft Label Correlation Learning and Label Noise Resistance

arXiv: https://arxiv.org/abs/2301.03283 · HTML: https://mlsanity.com/paper/2301.03283/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 97 references that are in the map.
- First posted on arXiv 9 Jan 2023.

## Builds on (most cited first, 14 of 14)

- [The Emerging Trends of Multi-Label Learning](https://mlsanity.com/paper/2011.11197/) (2020)
- [Multi-Label Learning from Single Positive Labels](https://mlsanity.com/paper/2106.09708/) (2021)
- [Multi-Label Learning with Global and Local Label Correlation](https://mlsanity.com/paper/1704.01415/) (2017)
- [Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding](https://mlsanity.com/paper/1911.00232/) (2019)
- [A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label Learning](https://mlsanity.com/paper/2005.00976/) (2020)
- [Multi-label Iterated Learning for Image Classification with Label Ambiguity](https://mlsanity.com/paper/2111.12172/) (2021)
- [On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification](https://mlsanity.com/paper/2207.13975/) (2022)
- [Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification](https://mlsanity.com/paper/2012.10715/) (2020)
- [Fast Multi-Instance Multi-Label Learning](https://mlsanity.com/paper/1310.2049/) (2013)
- [Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers](https://mlsanity.com/paper/2006.03833/) (2020)
- [CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise](https://mlsanity.com/paper/2105.07338/) (2021)
- [Transfer Representation Learning with TSK Fuzzy System](https://mlsanity.com/paper/1901.02703/) (2019)
- [Multi-View Fuzzy Logic System with the Cooperation between Visible and Hidden Views](https://mlsanity.com/paper/1807.08595/) (2018)
- [Evolving Multi-Label Fuzzy Classifier](https://mlsanity.com/paper/2203.15318/) (2022)

## Built on by (most cited first, 1 of 1)

- [Fuzzy Label: From Concept to Its Application in Label Learning](https://mlsanity.com/paper/2511.07165/) (2025)

## Abstract (arXiv)

Model transparency, label correlation learning and the robust-ness to label noise are crucial for multilabel learning. However, few existing methods study these three characteristics simultaneously. To address this challenge, we propose the robust multilabel Takagi-Sugeno-Kang fuzzy system (R-MLTSK-FS) with three mechanisms. First, we design a soft label learning mechanism to reduce the effect of label noise by explicitly measuring the interactions between labels, which is also the basis of the other two mechanisms. Second, the rule-based TSK FS is used as the base model to efficiently model the inference relationship be-tween features and soft labels in a more transparent way than many existing multilabel models. Third, to further improve the performance of multilabel learning, we build a correlation enhancement learning mechanism based on the soft label space and the fuzzy feature space. Extensive experiments are conducted to demonstrate the superiority of the proposed method.