5 research outputs found
A novel multi-task TSK fuzzy classifier and its enhanced version for labeling-risk-aware multi-task classification
202311 bckwAccepted ManuscriptOthersHong Kong Polytechnic University; UGCGRF; National Natural Science Foundation of China; Natural Science Foundation of Jiangsu Province; Jiangsu Province Outstanding Youth Fund; Fundamental Research Funds for the Central UniversitiesPublishedGreen (AAM
Multi-Label Takagi-Sugeno-Kang Fuzzy System
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.Comment: This work has been accepted by IEEE Transactions on Fuzzy System
A Robust Multilabel Method Integrating Rule-based Transparent Model, Soft Label Correlation Learning and Label Noise Resistance
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.Comment: This paper has been accepted by IEEE Transactions on Fuzzy System