Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network

Souza, Paulo Vitor de Campos; Soares, Eduardo; Guimarães, Augusto Júnio; Araújo, Vanessa Souza; Araújo, Vinícius Jonathan Silva; Rezende, Thiago Silva

doi:10.1007/s12530-020-09336-3

Cited by 5 publications

(2 citation statements)

References 73 publications

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“… 2020 ; de Campos Souza et al. 2021a , b , c ; de Campos Souza and Lughofer 2022b ), polynomial nodes Zhang et al. ( 2020 ), Bayesian approaches (Souza et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Evolving fuzzy neural classifier that integrates uncertainty from human-expert feedback

Souza

Lughofer

2022

Evolving Systems

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Evolving fuzzy neural networks are models capable of solving complex problems in a wide variety of contexts. In general, the quality of the data evaluated by a model has a direct impact on the quality of the results. Some procedures can generate uncertainty during data collection, which can be identified by experts to choose more suitable forms of model training. This paper proposes the integration of expert input on labeling uncertainty into evolving fuzzy neural classifiers (EFNC) in an approach called EFNC-U. Uncertainty is considered in class label input provided by experts, who may not be entirely confident in their labeling or who may have limited experience with the application scenario for which the data is processed. Further, we aimed to create highly interpretable fuzzy classification rules to gain a better understanding of the process and thus to enable the user to elicit new knowledge from the model. To prove our technique, we performed binary pattern classification tests within two application scenarios, cyber invasion and fraud detection in auctions. By explicitly considering class label uncertainty in the update process of the EFNC-U, improved accuracy trend lines were achieved compared to fully (and blindly) updating the classifiers with uncertain data. Integration of (simulated) labeling uncertainty smaller than 20% led to similar accuracy trends as using the original streams (unaffected by uncertainty). This demonstrates the robustness of our approach up to this uncertainty level. Finally, interpretable rules were elicited for a particular application (auction fraud identification) with reduced (and thus readable) antecedent lengths and with certainty values in the consequent class labels. Additionally, an average expected uncertainty of the rules were elicited based on the uncertainty levels in those samples which formed the corresponding rules.

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“… 2020 ; de Campos Souza et al. 2021a , b , c ; de Campos Souza and Lughofer 2022b ), polynomial nodes Zhang et al. ( 2020 ), Bayesian approaches (Souza et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Evolving fuzzy neural classifier that integrates uncertainty from human-expert feedback

Souza

Lughofer

2022

Evolving Systems

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“…In de Campos Souza [15,27] the FNN recognize pattern classification problem with the similar architecture of this paper. Other excellent cases are the pattern classification model that uses logical neurons proposed by de Campos Souza et al to solve Optical Interconnection Network problems [14]. Most of the models listed above have differences in their architecture, where three and four-layer models stand out.…”

Section: Fuzzy Neural Networkmentioning

confidence: 99%

Evolving Fuzzy Neural Network Based on Uni-nullneuron to Identify Auction Fraud

Souza

Lughofer

Guimarães³

2021

Atlantis Studies in Uncertainty Modelling

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The increase in transactions on the Internet related to the purchase of products or services can provide facilities for the parties involved in these acquisitions, but they also generate uncertainties and possibilities of attacks that can originate from fraud. This work seeks to explore and extract knowledge of auction fraud by using an evolving fuzzy neural network model based on n-uninorms. This new model uses a fuzzification technique based on Typicality and Eccentricity Data Analysis operators and a parallel processor for stream samples. To test the model in solving auction fraud problems, stateof-the-art neuro-fuzzy models were used to compare a public dataset on the topic. The results of the model proposed in this paper were superior to the other models evaluated (close to 96% accuracy) in the test, and the fuzzy rules demonstrate the model's ability to extract knowledge.

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A dropout weight-constrained recurrent neural network model for forecasting the price of major cryptocurrencies and CCi30 index

et al. 2021

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Autonomous Data Density pruning fuzzy neural network for Optical Interconnection Network

Cited by 5 publications

References 73 publications

Evolving fuzzy neural classifier that integrates uncertainty from human-expert feedback

Evolving fuzzy neural classifier that integrates uncertainty from human-expert feedback

Evolving Fuzzy Neural Network Based on Uni-nullneuron to Identify Auction Fraud

A dropout weight-constrained recurrent neural network model for forecasting the price of major cryptocurrencies and CCi30 index

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