Recurrent Classifier Based on an Incremental Metacognitive-Based Scaffolding Algorithm

Pratama, Mahardhika; Anavatti, Sreenatha G.; Lu, Jie

doi:10.1109/tfuzz.2015.2402683

Cited by 70 publications

(33 citation statements)

References 42 publications

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“…pENsemble+ features an online active learning scenario based on the extended conflict ignorance (ECI) paradigm [32] which evaluates conflict in both feature and target domain. This strategy was derived from the conflict and ignorance method for the conventional TSK fuzzy classifier [33] where the underlying difference lies in the use of a dynamic sampling paradigm [34] and Bayesian posterior probability estimation in both input and output space. None of these works, however, investigate the ECI method within the context of ensemble classifier.…”

Section: B Ensemble Learning Scenariomentioning

confidence: 99%

Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Pratama

Dimla

Tjahjowidodo

et al. 2020

IEEE Trans. Cybern.

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Section: B Ensemble Learning Scenariomentioning

confidence: 99%

Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Pratama

Dimla

Tjahjowidodo

et al. 2020

IEEE Trans. Cybern.

Self Cite

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“…More recent approaches for evolving fuzzy modeling include, to mention a few, the generic evolving neuro-fuzzy inference system (GENEFIS) [44], the parsimonious network based on fuzzy inference system (PANFIS) [45], the generalized smart evolving fuzzy system [46], the evolving fuzzy-rule-based parsimonious classifier (pClass) [47], and the meta-cognitive-based scaffolding recurrent classifier (rClass) [48]. Other important instances of evolving mechanisms are addressed in [41], [42].…”

Section: B Evolving Fuzzy System Modelsmentioning

confidence: 99%

Evolving Possibilistic Fuzzy Modeling for Realized Volatility Forecasting With Jumps

Maciel

Ballini

Gomide

2017

IEEE Trans. Fuzzy Syst.

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Equity assets volatility modeling and forecasting provide key information for risk management, portfolio construction, financial decision making and derivatives pricing. Realized volatility models outperform autoregressive conditional heteroskedasticity and stochastic volatility models in out-ofsample forecasting. Gain in forecasting performance is achieved when models comprise volatility jump components. This paper suggests evolving possibilistic fuzzy modeling to forecast realized volatility with jumps. The modeling approach is based on an extension of the possibilistic fuzzy c-means clustering and on functional fuzzy rule-based models. It employs memberships and typicalities to recursively update cluster centers. The evolving nature of the model allows adding or removing clusters using statistical distance-like criteria to update the model as dictated by input data. The possibilistic model improves robustness to noisy data and outliers, an essential requirement in financial markets volatility modeling and forecasting. Computational experiments and statistical analysis are done using Value-at-Risk estimates to evaluate and to compare the performance of the evolving possibilistic fuzzy modeling with the Heterogeneous Autoregressive, neural networks models and current state of the art evolving fuzzy methods. The experiments use actual data from S&P 500 and Nasdaq (United States), FTSE (United Kingdom), DAX (Germany), IBEX (Spain) and Ibovespa (Brazil), major equity market indexes in global markets. The results show that the evolving possibilistic fuzzy model is highly efficient to model realized volatility with jumps in terms of forecasting accuracy.

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“…Most of them are based on rejection principles, like the single-pass active learning approach of Lughofer [52], employing reliability concepts in the form of confusion and distance rejection (so-called conflict and ignorance models); on confusion measure [53]. Active learning modules can also be incorporated into meta-cognitive learning algorithm [54,51].…”

Section: Related Workmentioning

confidence: 99%

Online active supervision of an evolving classifier for customized-gesture-command learning

Bouillon

Anquetil

2017

Neurocomputing

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To cite this version:Manuel Bouillon, Eric Anquetil.Online active supervision of an evolving classifier for customized-gesture-command learning.Neurocomputing AbstractTouch sensitive interfaces enable new interaction methods, like gesture commands. For users to easily memorize more than a dozen of gesture commands, it is important to enable gesture set customization. The classifier used to recognize drawn symbols must hence be customizable -able to learn from very few data -and evolving -able to learn new classes on-the-fly and improve during its use. The objective of this work is to obtain a gesture command system that cooperates as best as possible with the user: that learns from its mistakes without soliciting the user too often. This paper presents a novel approach for the online active learning of gesture commands, with three contributions. The IntuiSup supervisor monitors the learning process and user interactions. The Evolving Sampling by Uncertainty (ESU) algorithm enables to maintain the error/interaction compromise over time. The Boosted-ESU (B-ESU) method optimizes interaction impact to fasten system learning speed. The efficiency of our approach is evaluated on the publicly available ILG Data Base of gesture commands. Experimentation shows the effectiveness of the supervision strategy and improvements both in term of accuracy and learning speed.

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Recurrent Classifier Based on an Incremental Metacognitive-Based Scaffolding Algorithm

Cited by 70 publications

References 42 publications

Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Evolving Possibilistic Fuzzy Modeling for Realized Volatility Forecasting With Jumps

Online active supervision of an evolving classifier for customized-gesture-command learning

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