Multivariable Gaussian Evolving Fuzzy Modeling System

Lemos, André Paim; Caminhas, Walmir M.; Gomide, Fernando

doi:10.1109/tfuzz.2010.2087381

Cited by 185 publications

(103 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An evolving version of Vector quantization was designed in [41] and is algorithmic backbone of FLEXFIS [42], which was later extended to a more robust version including rule merging in [43], generalized rules and an incremental feature weighting mechanism in [44]. A generalized TSK fuzzy rule was put forward in [45]- [47] and generates a non-axis parallel ellipsoidal cluster, which happens to have better coverage and flexibility than conventional fuzzy rules [44]. Pratama et al in [47] developed the theory of rule statistical contribution borrowing the concept of hidden neuron statistical contribution in [48], [49].…”

Section: Introductionmentioning

confidence: 99%

Evolving Ensemble Fuzzy Classifier

Pratama

Pedrycz

Lughofer

2018

IEEE Trans. Fuzzy Syst.

101

View full text Add to dashboard Cite

Abstract-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 nonstationary 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's 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.

show abstract

Section: Introductionmentioning

confidence: 99%

Evolving Ensemble Fuzzy Classifier

Pratama

Pedrycz

Lughofer

2018

IEEE Trans. Fuzzy Syst.

101

View full text Add to dashboard Cite

show abstract

“…For any new sample, a new rule is recruited when the criteria of adding rule are satisfied and then its antecedent parameters are determined with the newly loaded sample based on (19). In this way, the antecedent parameters are permanently updated whenever a new rule is generated.…”

Section: B Rules Evolvingmentioning

confidence: 99%

“…To improve the performance of SAFIS, the author has developed an extended SAFIS (ESAFIS) [15] based on the modified influence of a rule in which the uniform distribution of the input data is not necessary. In [19], a multivariable Gaussian evolving fuzzy modeling system (eMG) is developed using a recursive clustering algorithm inspired by the idea of participatory learning. A evolving fuzzy model (eFuMo) method [20] is proposed for a monitoring system based on the normalized Mahalanobis distance.…”

Section: Introductionmentioning

confidence: 99%

Correntropy-Based Evolving Fuzzy Neural System

Bao

Rong

Angelov

et al. 2018

IEEE Trans. Fuzzy Syst.

View full text Add to dashboard Cite

Abstract-In this paper, a correntropy-based evolving fuzzy neural system (correntropy-EFNS) is proposed for approximation of nonlinear systems. Different from the commonly used meansquare error criterion, correntropy has a strong outliers rejection ability through capturing the higher moments of the error distribution. Considering the merits of correntropy, this paper brings contributions to build EFNS based on the correntropy concept to achieve a more stable evolution of the rule base and update of the rule parameters instead of the commonly used meansquare error criterion. The correntropy-EFNS (CEFNS) begins with an empty rule base and all rules are evolved online based on the correntropy criterion. The consequent part parameters are tuned based on the maximum correntropy criterion where the correntropy is used as the cost function so as to improve the nonGaussian noise rejection ability. The steady-state convergence performance of the CEFNS is studied through the calculation of the steady-state excess mean square error (EMSE) in two cases: i) Gaussian noise; and ii) non-Gaussian noise. Finally, the CEFNS is validated through a benchmark system identification problem, a Mackey-Glass time series prediction problem as well as five other real-world benchmark regression problems under both noise-free and noisy conditions. Compared with other evolving fuzzy neural systems, the simulation results show that the proposed CEFNS produces better approximation accuracy using the least number of rules and training time and also owns superior non-Gaussian noise handling capability.

show abstract

“…A number of innovative DSA operational analysis are considered for diversified BI service applications, such as, product life cycle management service that uses closed-loop PLM framework [41], transport logistic service that implements an IoE based ontology framework [42], and a supply chain management service that uses a cognitive based smart logistic framework [43].…”

Section: Context Of Data Science and Analyticsmentioning

confidence: 99%

Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams

Mishra¹

2018

IJSRCSE

View full text Add to dashboard Cite

Abstract-The latest internet of everything (IoE) advancements in data elicitation and digital storage technology leads to a large heterogeneous data depository, in which the IoE data are stored in a column oriented relational framework. The main purposes of the research are to design and explore data models, frameworks, architectures, and algorithms on network-centric data, mainly IoE data to accomplish the data science and knowledge analytic tasks for Intellectual domain applications. Some storage incompatibilities are there in the relational structure of multi-objective IoE data base that creates threats to data integrity and consistency. In a large scale IoE database, huge numbers of rows are there along with limited number of columns. So, column oriented relational framework greatly improve the performance of IoE data base in terms of data depository and access management. Knowledge analytic is the major part of data science; Analytic is a never ending process because of progressive technological change requirements as well as the business change requirements. The beauty of Analytics is that two data scientist with same problem may come up with two different new solutions. So, in this work, I discuss the overall data science and knowledge analytic streams for an effective IoE database management and knowledge discovery.

show abstract

Multivariable Gaussian Evolving Fuzzy Modeling System

Cited by 185 publications

References 17 publications

Evolving Ensemble Fuzzy Classifier

Evolving Ensemble Fuzzy Classifier

Correntropy-Based Evolving Fuzzy Neural System

Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams

Contact Info

Product

Resources

About