Search

Your search keyword '"Pedrycz, Witold"' showing total 60,763 results
Start Over Author "Pedrycz, Witold"

Refine your results

more »

more »

more »

more »
60,763 results on '"Pedrycz, Witold"'

309. Introduction

Author

Chen, Luefeng, Wu, Min, Pedrycz, Witold, Hirota, Kaoru, Kacprzyk, Janusz, Series Editor, Chen, Luefeng, Wu, Min, Pedrycz, Witold, and Hirota, Kaoru

Published
2021
Full Text
View/download PDF

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

Author

Pratama, Mahardhika, Pedrycz, Witold, and Webb, Geoffrey I.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
Abstract

Existing 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 FNN, namely DEVFNN. Fuzzy rules can be automatically extracted from data streams or removed if they play limited 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 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 dimensionality of input space 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 seven datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four popular continual learning algorithms 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., Comment: This paper has been published in IEEE Transactions on Fuzzy Systems

Published
2018

Catalog

Books, media, physical & digital resources
See catalog results