Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey

Major assumptions in computational intelligence and machine learning consist of the availability of a historical dataset for model development, and that the resulting model will, to some extent, handle similar instances during its online operation. However, in many real world applications, these assumptions may not hold as the amount of previously available data may be insufficient to represent the underlying system, and the environment and the system may change over time. As the amount of data increases, it is no longer feasible to process data efficiently using iterative algorithms, which typically require multiple passes over the same portions of data. Evolving modeling from data streams has emerged as a framework to address these issues properly by self-adaptation, single-pass learning steps and evolution as well as contraction of model components on demand and on the fly. This survey focuses on evolving fuzzy rule-based models and neuro-fuzzy networks for clustering, classification and regression and system identification in online, real-time environments where learning and model development should be performed incrementally. (C) 2019 Published by Elsevier Inc.Igor Škrjanc, Jose Antonio Iglesias and Araceli Sanchis would like to thank to the Chair of Excellence of Universidad Carlos III de Madrid, and the Bank of Santander Program for their support. Igor Škrjanc is grateful to Slovenian Research Agency with the research program P2-0219, Modeling, simulation and control. Daniel Leite acknowledges the Minas Gerais Foundation for Research and Development (FAPEMIG), process APQ-03384-18. Igor Škrjanc and Edwin Lughofer acknowledges the support by the ”LCM — K2 Center for Symbiotic Mechatronics” within the framework of the Austrian COMET-K2 program. Fernando Gomide is grateful to the Brazilian National Council for Scientific and Technological Development (CNPq) for grant 305906/2014-3

Combining semantic web technologies with evolving fuzzy classifier eClass for EHR-based phenotyping : a feasibility study

In parallel to nation-wide efforts for setting up shared electronic health records (EHRs) across healthcare settings, several large-scale national and international projects are developing, validating, and deploying electronic EHR oriented phenotype algorithms that aim at large-scale use of EHRs data for genomic studies. A current bottleneck in using EHRs data for obtaining computable phenotypes is to transform the raw EHR data into clinically relevant features. The research study presented here proposes a novel combination of Semantic Web technologies with the on-line evolving fuzzy classifier eClass to obtain and validate EHR-driven computable phenotypes derived from 1956 clinical statements from EHRs. The evaluation performed with clinicians demonstrates the feasibility and practical acceptability of the approach proposed

Evolving Ensemble Fuzzy Classifier

The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it addresses the bias and variance dilemma better than its single model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining non-stationary data streams can be found in the literature, most of them are crafted under a static base classifier and revisits preceding samples in the sliding window for a retraining step. This feature causes computationally prohibitive complexity and is not flexible enough to cope with rapidly changing environments. Their complexities are often demanding because it involves a large collection of offline classifiers due to the absence of structural complexities reduction mechanisms and lack of an online feature selection mechanism. A novel evolving ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in this paper. pENsemble differs from existing architectures in the fact that it is built upon an evolving classifier from data streams, termed Parsimonious Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism, which estimates a localized generalization error of a base classifier. A dynamic online feature selection scenario is integrated into the pENsemble. This method allows for dynamic selection and deselection of input features on the fly. pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble structure. The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System

Self-Organizing Fuzzy Inference Ensemble System for Big Streaming Data Classification

An evolving intelligent system (EIS) is able to self-update its system structure and meta-parameters from streaming data. However, since the majority of EISs are implemented on a single-model architecture, their performances on large-scale, complex data streams are often limited. To address this deficiency, a novel self-organizing fuzzy inference ensemble framework is proposed in this paper. As the base learner of the proposed ensemble system, the self-organizing fuzzy inference system is capable of self-learning a highly transparent predictive model from streaming data on a chunk-by-chunk basis through a human-interpretable process. Very importantly, the base learner can continuously self-adjust its decision boundaries based on the inter-class and intra-class distances between prototypes identified from successive data chunks for higher classification precision. Thanks to its parallel distributed computing architecture, the proposed ensemble framework can achieve great classification precision while maintain high computational efficiency on large-scale problems. Numerical examples based on popular benchmark big data problems demonstrate the superior performance of the proposed approach over the state-of-the-art alternatives in terms of both classification accuracy and computational efficiency

Implementation and analysis of evolving classifier algorithms in high dimensional space

Orientador: Fernando Antônio Campos GomideDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Sistemas evolutivos e processamento de dados de alta dimensão são de grande importância prática, atualmente sob intensa investigação. Esta dissertação introduz um neuro classificador evolutivo, avalia seu desempenho usando dados de alta dimensão e compara seu desempenho com classificadores evolutivos e clássicos representativos do estado da arte na área. O neuro classificador processa fluxos de dados continuamente e determina a estrutura de uma rede neural artificial e seus respectivos pesos sinápticos. Os resultados de simulação sugerem que o algoritmo proposto é competitivo quando comparado com os modelos evolutivos analisados nesta dissertação. Ele supera, em termos de taxa de classificação, todos os modelos na maioria dos conjuntos de dados considerados. Ainda, o neuro classificador requer um menor tempo de processamento por amostra entre os classificadores evolutivos e os clássicos não evolutivosAbstract: Evolving systems and high dimensional stream data processing algorithms are of enormous practical importance, and currently are under intensive investigation. This dissertation in- troduces an evolving neural classifying approach, evaluates its performance using high dimensional data, and compare its performance with evolving and classic classifier algorithms representative of the state of the art. The evolving neural classifier works in one-pass mode to find the neural network structure and its weights using high dimensional stream data. The results achieved by the proposed approach suggests that it is competitive with the evolving models addressed in this dissertation. It outperforms in classification rate all of them in most of the datasets considered. Also, the approach requires the lowest per sample processing time amongst the evolving and classic batch classifiersMestradoAutomaçãoMestre em Engenharia Elétrica156374/2014-5CNP

Evolving Fuzzy Classifiers: Application to Incremental Learning of Handwritten Gesture Recognition Systems

International audienceIn this paper, we present a new method to design customizable self-evolving fuzzy rule-based classifiers. The presented approach combines an incremental clustering algorithm with a fuzzy adaptation method in order to learn and maintain the model. We use this method to build an evolving handwritten gesture recognition system. The self-adaptive nature of this system allows it to start its learning process with few learning data, to continuously adapt and evolve according to any new data, and to remain robust when introducing a new unseen class at any moment in the life-long learning process

Self-Organizing Fuzzy Belief Inference System for Classification

Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data prediction. In this paper, a novel zero-order EFS with a unique belief structure is proposed for data stream classification. Thanks to this new belief structure, the proposed model can handle the inter-class overlaps in a natural way and better capture the underlying multi-model structure of data streams in the form of prototypes. Utilizing data-driven soft thresholds, the proposed model self-organizes a set of prototype-based IF-THEN fuzzy belief rules from data streams for classification, and its learning outcomes are practically meaningful. With no requirement of prior knowledge in the problem domain, the proposed model is capable of self-determining the appropriate level of granularity for rule base construction, while enabling users to specify their preferences on the degree of fineness of its knowledge base. Numerical examples demonstrate the superior performance of the proposed model on a wide range of stationary and nonstationary classification benchmark problems

Adaptive input selection and evolving neural fuzzy networks modeling

This paper suggests an evolving approach to develop neural fuzzy networks for system modeling. The approach uses an incremental learning procedure to simultaneously select the model inputs, to choose the neural network structure, and to update the network weights. Candidate models with larger and smaller number of input variables than the current model are constructed and tested concurrently. The procedure employs a statistical test in each learning step to choose the best model amongst the current and candidate models. Membership functions can be added or deleted to adjust input space granulation and the neural network structure. Granulation and structure adaptation depend of the modeling error. The weights of the neural networks are updated using a gradient-descent algorithm with optimal learning rate. Prediction and nonlinear system identification examples illustrate the usefulness of the approach. Comparisons with state of the art evolving fuzzy modeling alternatives are performed to evaluate performance from the point of view of modeling error. Simulation results show that the evolving adaptive input selection modeling neural network approach achieves as high as, or higher performance than the remaining evolving modeling methods81314CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE MINAS GERAIS - FAPEMIG305906/2014-3não temnão te

Autonomous learning multi-model systems from data streams

In this paper, an approach to autonomous learning of a multi-model system from streaming data, named ALMMo, is proposed. The proposed approach is generic and can easily be applied also to probabilistic or other types of local models forming multi-model systems. It is fully data-driven and its structure is decided by the nonparametric data clouds extracted from the empirically observed data without making any prior assumptions concerning data distribution and other data properties. All meta-parameters of the proposed system are obtained directly from the data and can be updated recursively, which improves memory- and calculation-efficiency of the proposed algorithm. The structural evolution mechanism and online data cloud quality monitoring mechanism of the ALMMo system largely enhance the ability of handling shifts and/or drifts in the streaming data pattern. Numerical examples of the use of ALMMo system for streaming data analytics, classification and prediction are presented as a proof of the proposed concept