Multiclass Fuzzily Weighted Adaptive-Boosting-Based Self-Organizing Fuzzy Inference Ensemble Systems for Classification

Gu, Xiaowei; Angelov, Plamen

doi:10.1109/tfuzz.2021.3126116

Cited by 15 publications

(22 citation statements)

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“…There have been a few ensemble models that employ fuzzy systems as ensemble components to learn from (big) data streams (Scherer 2011;Soua et al 2013;Iglesias et al 2013b;Leite and Škrjanc 2019;Gu et al 2021b;Lughofer et al 2021;Lughofer and Pratama 2022), offering both great precision and high interpretability. Although the existing works on ensemble fuzzy models have reported promising results, only very few efforts have been made attempting to design novel ensemble frameworks specifically for better incorporating fuzzy systems (Gu and Angelov 2021). Hence, the potential of fuzzy rule-based systems in ensemble learning has not been fully explored.…”

Section: Challenges and Directions For Further Research And Developmentmentioning

confidence: 99%

Autonomous learning for fuzzy systems: a review

Han

Shen

et al. 2022

Artif Intell Rev

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As one of the three pillars in computational intelligence, fuzzy systems are a powerful mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy systems take the form of linguistic IF-THEN fuzzy rules that are easy to understand for human. In this sense, fuzzy inference mechanisms have been developed to mimic human reasoning and decision-making. From a data analytic perspective, fuzzy systems provide an effective solution to build precise predictive models from imprecise data with great transparency and interpretability, thus facilitating a wide range of real-world applications. This paper presents a systematic review of modern methods for autonomously learning fuzzy systems from data, with an emphasis on the structure and parameter learning schemes of mainstream evolving, evolutionary, reinforcement learning-based fuzzy systems. The main purpose of this paper is to introduce the underlying concepts, underpinning methodologies, as well as outstanding performances of the state-of-the-art methods. It serves as a one-stop guide for readers learning the representative methodologies and foundations of fuzzy systems or who desire to apply fuzzy-based autonomous learning in other scientific disciplines and applied fields.

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Section: Challenges and Directions For Further Research And Developmentmentioning

confidence: 99%

Autonomous learning for fuzzy systems: a review

Han

Shen

et al. 2022

Artif Intell Rev

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“…The possibility of constructing deep ensemble models with fuzzy systems is firstly explored in [45], resulting in a multi-layered ensemble evolving fuzzy model that can learn multi-layered distributed representations from data for classification. A fuzzily weighted adaptive boosting (FWAdaBoost) algorithm that utilizes confidence scores produced by zero-order EFSs in both weight updating and ensemble output generation to create stronger ensemble evolving fuzzy classifier is introduced in [46]. In [47], an online bagging-based ensemble fuzzy classifier that can autonomous prune base fuzzy classifiers with higher prediction errors is presented.…”

Section: Introductionmentioning

confidence: 99%

Semisupervised Fuzzily Weighted Adaptive Boosting for Classification

Gu,

Angelov,

Shen

2024

IEEE Trans. Fuzzy Syst.

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Fuzzy systems offer a formal and practically popular methodology for modelling nonlinear problems with inherent uncertainties, entailing strong performance and model interpretability. Particularly, semi-supervised boosting is widely recognised as a powerful approach for creating stronger ensemble classification models in the absence of sufficient labelled data without introducing any modification to the employed base classifiers. However, the potential of fuzzy systems in semisupervised boosting has not been systematically explored yet. In this study, a novel semi-supervised boosting algorithm devised for zero-order evolving fuzzy systems is proposed. It ensures both the consistence amongst predictions made by individual base classifiers at successive boosting iterations and the respective levels of confidence towards their predictions throughout the process of sample weight updating and ensemble output generation. In so doing, the base classifiers are empowered to gradually focus more on challenging samples that are otherwise hard to generalise, enabling the development of more precise integrated classification boundaries. Numerical evaluations on a range of benchmark problems are carried out, demonstrating the efficacy of the proposed semi-supervised boosting algorithm for constructing ensemble fuzzy classifiers with high accuracy.

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“…An ensemble pruning scheme is also introduced to pENsemble to remove base classifiers of a lower accuracy. A fuzzily weighted adaptive boosting scheme designed specifically for zero-order EISs to construct stronger ensemble classifiers is introduced in [30]. This novel boosting utilises the confidence scores produced by EISs in both weight updating and ensemble output generation to create more precise classification boundaries.…”

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confidence: 99%

“…Furthermore, the following six ensemble models are also used for performance comparison: (9) random forest (RF) [7]; (10) fuzzily weighted AdaBoost-based SOFIS ensemble (FWADBSOFIS) [30]; (11) AdaBoost.M2-based KNN ensemble (ADBKNN) [66]; (12) SAMME-based KNN ensemble (SAMKNN) [67]; (13) AdaBoost.M2-based SVM ensemble (ADBSVM) [66], and; (14) SAMME-based SVM ensemble (SAMSVM) [67].…”

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Multiobjective Evolutionary Optimization for Prototype-Based Fuzzy Classifiers

Shen

et al. 2023

IEEE Trans. Fuzzy Syst.

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Evolving intelligent systems (EISs), particularly, the zero-order ones have demonstrated strong performance on many real-world problems concerning data stream classification, while offering high model transparency and interpretability thanks to their prototype-based nature. Zero-order EISs typically learn prototypes by clustering streaming data online in a "one pass" manner for greater computation efficiency. However, such identified prototypes often lack optimality, resulting in less precise classification boundaries, thereby hindering the potential classification performance of the systems. To address this issue, a commonly adopted strategy is to minimise the training error of the models on historical training data or alternatively, to iteratively minimise the intra-cluster variance of the clusters obtained via online data partitioning. This recognises the fact that the ultimate classification performance of zero-order EISs is driven by the positions of prototypes in the data space. Yet, simply minimising the training error may potentially lead to overfitting, whilst minimising the intra-cluster variance does not necessarily ensure the optimised prototype-based models to attain improved classification outcomes. To achieve better classification performance whilst avoiding overfitting for zero-order EISs, this paper presents a novel multi-objective optimisation approach, enabling EISs to obtain optimal prototypes via involving these two disparate but complementary strategies simultaneously. Five decision-making schemes are introduced for selecting a suitable solution to deploy from the final non-dominated set of the resulting optimised models. Systematic experimental studies are carried out to demonstrate the effectiveness of the proposed optimisation approach in improving the classification performance of zero-order EISs.

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Multiclass Fuzzily Weighted Adaptive-Boosting-Based Self-Organizing Fuzzy Inference Ensemble Systems for Classification

Cited by 15 publications

References 58 publications

Autonomous learning for fuzzy systems: a review

Autonomous learning for fuzzy systems: a review

Semisupervised Fuzzily Weighted Adaptive Boosting for Classification

Multiobjective Evolutionary Optimization for Prototype-Based Fuzzy Classifiers

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