An Improving Data Stream Classification Algorithm Based on BP Neural Network

2019 ◽  
pp. 145-153
Author(s):  
Baoju Zhang ◽  
Guilin Wang ◽  
Lei Xue
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Data Stream ◽  
Classification Algorithm ◽  
Stream Classification ◽  
Data Stream Classification

The Research of Data Stream Classification Based on Rough Set Theory-Neural Network Integration

2013 ◽  
Vol 441 ◽  
pp. 717-720
Author(s):  
Zhi Bo Ren ◽  
Chun Miao Yan ◽  
Yu Zhou Wei ◽  
Lei Sun
Keyword(s):  
Neural Network ◽  
Set Theory ◽  
Rough Set ◽  
Data Stream ◽  
High Speed ◽  
Rough Set Theory ◽  
Classification Model ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Voting Rule

According to the high speed of data arriving, a large amount of data and concept drifting in the stream model, combining the techniques of rough set theory, neural network and voting rule, we put forward a new data stream classification model, which is a multi-classifier integration based on rough set theory, neural network. Firstly, it reduces all attributes using rough set theory; secondly, it constructs base classifiers on the data chunks after the reduction of attributes using the improved BP neural network; finally, it fuses various base classifiers into an ensemble by voting rule. Through applying the model to classify data stream, the experiment results show that the ensemble method is feasible and effective.

Implementation of Data Stream Classification Neural Network Models Over Big Data Platforms

2021 ◽  
pp. 272-280
Author(s):  
Fernando Puentes-Marchal ◽  
María Dolores Pérez-Godoy ◽  
Pedro González ◽  
María José Del Jesus
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Data Stream ◽  
Network Models ◽  
Neural Network Models ◽  
Stream Classification ◽  
Data Stream Classification

scholarly journals A Skip-Connected Evolving Recurrent Neural Network for Data Stream Classification under Label Latency Scenario

2020 ◽  
Vol 34 (04) ◽  
pp. 3717-3724 ◽  
Author(s):  
Monidipa Das ◽  
Mahardhika Pratama ◽  
Jie Zhang ◽  
Yew Soon Ong
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Data Stream ◽  
Mapping Function ◽  
Network Capacity ◽  
Current Data ◽  
Network Parameter ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Real World Datasets

Stream classification models for non-stationary environments often assume the immediate availability of data labels. However, in a practical scenario, it is quite natural that the data labels are available only after some temporal lag. This paper explores how a stream classifier model can be made adaptive to such label latency scenario. We propose SkipE-RNN, a self-evolutionary recurrent neural network with dynamically evolving skipped-recurrent-connection for the best utilization of previously observed label information while classifying the current data. When the data label is unavailable, SkipE-RNN uses an auto-learned mapping function to find the best match from the already known data labels and updates the network parameter accordingly. Later, upon availability of true data label, if the previously mapped label is found to be incorrect, SkipE-RNN employs a regularization technique along with the parameter updating process, so as to penalize the model. In addition, SkipE-RNN has inborn power of self-adjusting the network capacity by growing/pruning hidden nodes to cope with the evolving nature of data stream. Rigorous empirical evaluations using synthetic as well as real-world datasets reveal effectiveness of SkipE-RNN in both finitely delayed and infinitely delayed data label scenarios.

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