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

Evolving Big Data Stream Classification with MapReduce

2014 ◽  
Author(s):  
Ahsanul Haque ◽  
Brandon Parker ◽  
Latifur Khan ◽  
Bhavani Thuraisingham
Keyword(s):  
Big Data ◽  
Data Stream ◽  
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.

Data stream classification and big data analytics

2015 ◽  
Vol 150 ◽  
pp. 238-239 ◽  
Author(s):  
Bartosz Krawczyk ◽  
Jerzy Stefanowski ◽  
Michał Wozniak
Keyword(s):  
Big Data ◽  
Data Stream ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Stream Classification ◽  
Data Stream Classification

sl-LSTM

2020 ◽  
Vol 12 (3) ◽  
pp. 1-16
Author(s):  
Nancy Victor ◽  
Daphne Lopez
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Network Architecture ◽  
Network Models ◽  
Stochastic Gradient Descent ◽  
Sensitive Information ◽  
Neural Network Models ◽  
Digital World ◽  
Current Cell ◽  
Diverse Data

The volume of data in diverse data formats from various data sources has led the way for a new drift in the digital world, Big Data. This article proposes sl-LSTM (sequence labelling LSTM), a neural network architecture that combines the effectiveness of typical LSTM models to perform sequence labeling tasks. This is a bi-directional LSTM which uses stochastic gradient descent optimization and combines two features of the existing LSTM variants: coupled input-forget gates for reducing the computational complexity and peephole connections that allow all gates to inspect the current cell state. The model is tested on different datasets and the results show that the integration of various neural network models can further improve the efficiency of approach for identifying sensitive information in Big data.

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