Multilayer Ensemble Evolving Fuzzy Inference System

Gu, Xiaowei

doi:10.1109/tfuzz.2020.2988846

Cited by 31 publications

(6 citation statements)

References 43 publications

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“…MEEFIS—A multilayer ensemble evolving a fuzzy inference system primarily based on an ensemble of multiple-input multiple-output first-order evolving fuzzy inference systems (EFISs) prepared in a multilayered architecture. The parameters are the number of layers (default: 3), the number of training epochs (default: 3), and the forgetting factor (default: 0.1) [ 47 ].…”

Section: Methodsmentioning

confidence: 99%

An Explainable Evolving Fuzzy Neural Network to Predict the k Barriers for Intrusion Detection Using a Wireless Sensor Network

Souza

Lughofer

Batista

2022

Sensors

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Evolving fuzzy neural networks have the adaptive capacity to solve complex problems by interpreting them. This is due to the fact that this type of approach provides valuable insights that facilitate understanding the behavior of the problem being analyzed, because they can extract knowledge from a set of investigated data. Thus, this work proposes applying an evolving fuzzy neural network capable of solving data stream regression problems with considerable interpretability. The dataset is based on a necessary prediction of k barriers with wireless sensors to identify unauthorized persons entering a protected territory. Our method was empirically compared with state-of-the-art evolving methods, showing significantly lower RMSE values for separate test data sets and also lower accumulated mean absolute errors (MAEs) when evaluating the methods in a stream-based interleaved-predict-and-then-update procedure. In addition, the model could offer relevant information in terms of interpretable fuzzy rules, allowing an explainable evaluation of the regression problems contained in the data streams.

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Section: Methodsmentioning

confidence: 99%

An Explainable Evolving Fuzzy Neural Network to Predict the k Barriers for Intrusion Detection Using a Wireless Sensor Network

Souza

Lughofer

Batista

2022

Sensors

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“…It is well known that the power of DNNs comes from the multi-layered distributed representations. However, most of the existing ensemble fuzzy models are based on a flat structure, only very few works explore the possibility of constructing deep ensemble models with fuzzy systems (Pratama et al 2020b;Gu 2021).…”

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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“…Despite that the base models are all fed with the same data stream, each individual base model uses a different parameter setting from others, and the diversity between base models is thereby attained. 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].…”

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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Multilayer Ensemble Evolving Fuzzy Inference System

Cited by 31 publications

References 43 publications

An Explainable Evolving Fuzzy Neural Network to Predict the k Barriers for Intrusion Detection Using a Wireless Sensor Network

An Explainable Evolving Fuzzy Neural Network to Predict the k Barriers for Intrusion Detection Using a Wireless Sensor Network

Autonomous learning for fuzzy systems: a review

Semisupervised Fuzzily Weighted Adaptive Boosting for Classification

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