Deep Takagi–Sugeno–Kang Fuzzy Classifier With Shared Linguistic Fuzzy Rules

Zhang, Yuanpeng; Ishibuchi, Hisao; Wang, Shitong

doi:10.1109/tfuzz.2017.2729507

Cited by 137 publications

(56 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Heuristic processing is utilized in mapping. A shared fuzzy rule based TSK type fuzzy classifier is developed in [29]. TSK Fuzzy systems are better for the interpretability and accuracy attainment.…”

Section: Related Workmentioning

confidence: 99%

Interpretability and Accuracy Analysis of Fuzzy Rule Based System Designed for Abalone

Chandra*,

Agarwal,

Shukla

2020

IJRTE

View full text Add to dashboard Cite

 Abstract: Fuzzy Rule Based Systems are playing vital role in the implementation of human decision making. The development of interpretable Fuzzy Rule Based Systems with improved accuracy is a crucial research aspect in fuzzy based systems. Mamdani type fuzzy rule based systems are used to implement the proposed model. In this manuscript a FRBS is implemented with Guaje Open-Access Java based software. The interpretability and accuracy assessments are recorded on the different experiments with various rule generation methods, like Fuzzy decision tree and Wang Mendel method. The results are found satisfactory and a trade-off is handled between interpretability and accuracy. The major concern of the experimentation is number and type of fuzzy partitions. K-means and Hierarchical Fuzzy Partitions are used in the experiments with three and five number of fuzzy partitions.

show abstract

Section: Related Workmentioning

confidence: 99%

Interpretability and Accuracy Analysis of Fuzzy Rule Based System Designed for Abalone

Chandra*,

Agarwal,

Shukla

2020

IJRTE

View full text Add to dashboard Cite

show abstract

“…Moreover, our approach is equipped by the online feature selection and hidden layer merging scenarios which dynamically compress the input space of each layer. This mechanism replaces the random feature selection of the original work in [48], [50]. Our approach also operates in the one-pass learning scenario being compatible for online learning setting whereas [48], [50] still applies an offline working scenario.…”

Section: B Deep Stacked Networkmentioning

confidence: 99%

“…This approach adopts the stacked deep fuzzy neural network concept in [50] where the feature space of the bottom layer to the top one is growing incorporating the outputs of previous layers as extra input information. DEVFNN differs itself from [5], [48], [50] where it characterizes a fully flexible network structure targeted to address the requirement of continual learning [22]. This property is capable of expanding the depth of the DNN whenever the drift is identified to adapt to rapidly changing environments.…”

Section: Introductionmentioning

confidence: 99%

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Pratama

Pedrycz

Webb

2019

IEEE Trans. Fuzzy Syst.

View full text Add to dashboard Cite

Existing fuzzy neural networks (FNNs) are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep fuzzy neural network, namely deep evolving fuzzy neural networks (DEVFNN). Fuzzy rules can be automatically extracted from data streams or removed if they play little role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely Generic Classifier (gClass), drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of input space dimension due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using six datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four state-ofthe art data stream methods and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy. Moreover, it is also shown that the concept drift detection method is an effective tool to control the depth of network structure while the hidden layer merging scenario is capable of simplifying the network complexity of a deep network with negligible compromise of generalization performance.

show abstract

“…It is evident that the notion of random shifts in each layer of DSSCN improves linear separability of given classification problem because it paves a way to move apart the manifolds of original problem in a stacked manner [48]. This structure is also perceived as a sort of hierarchical structure but it differs from the conventional hierarchical deep neural network which generally loses transparency of intermediate features because it can no longer be associated with physical semantics of original input features [47]. It is also worth mentioning that DSSCN is equipped by the online feature selection mechanism coupled in every building unit.…”

Section: Deep Stacked Stochastic Configuration Networkmentioning

confidence: 99%

Deep stacked stochastic configuration networks for lifelong learning of non-stationary data streams

Pratama

Wang

2019

Information Sciences

View full text Add to dashboard Cite

The concept of stochastic configuration networks (SCNs) offers a solid framework for fast implementation of feedforward neural networks through randomized learning. Unlike conventional randomized approaches, SCNs provide an avenue to select appropriate scope of random parameters to ensure the universal approximation property. In this paper, a deep version of stochastic configuration networks, namely deep stacked stochastic configuration network (DSSCN), is proposed for modeling non-stationary data streams. As an extension of evolving stochastic configuration networks (eSCNs), this work contributes a way to grow and shrink the structure of deep stochastic configuration networks autonomously from data streams. The performance of DSSCN is evaluated by six benchmark datasets. Simulation results, compared with prominent data stream algorithms, show that the proposed method is capable of achieving comparable accuracy and evolving compact and parsimonious deep stacked network architecture.

show abstract

Deep Takagi–Sugeno–Kang Fuzzy Classifier With Shared Linguistic Fuzzy Rules

Cited by 137 publications

References 48 publications

Interpretability and Accuracy Analysis of Fuzzy Rule Based System Designed for Abalone

Interpretability and Accuracy Analysis of Fuzzy Rule Based System Designed for Abalone

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Deep stacked stochastic configuration networks for lifelong learning of non-stationary data streams

Contact Info

Product

Resources

About