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Cluster Computing
Erschienen in: Cluster Computing 2/2020

09.07.2019

ELM-NET, a closer to practice approach for classifying the big data using multiple independent ELMs

verfasst von: Amin Shokrzade, Fardin Akhlaghian Tab, Mohsen Ramezani

Erschienen in: Cluster Computing | Ausgabe 2/2020

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Abstract

In this paper, a new ELM based classification method is presented to deal with the large volume of data in an efficient way. By inspiration from both parallel and sequential ELMs, this method consists of some independent ELMs which are trained using data batches in parallel. The main goal of this method is preventing exponential training time by running some ELMs in parallel which similar to the sequential methods, are trained using different data chunks. Moreover, a new aggregation method is used here to outperform this structure which can relatively achieve stable results for the different number of the ELMs. The stable results can persuade us to use such classification method on regular platforms to decrease the cost of the big data analyzing. Our method is tested on different platforms to indicate that it can be used for reducing the costs of big data analyzing. Experimental results on MNIST, KDDCup99, KDDCup99_2, Susy, and Higgs datasets shows the better performance of our method than the state-of-the-art methods.
Vorheriger Artikel KPN-based parallelization of Wu–Manber algorithm on multi-core machines
Nächster Artikel Detection of premature ventricular contraction (PVC) using linear and nonlinear techniques: an experimental study

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Metadaten
Titel
ELM-NET, a closer to practice approach for classifying the big data using multiple independent ELMs
verfasst von
Amin Shokrzade
Fardin Akhlaghian Tab
Mohsen Ramezani
Publikationsdatum
09.07.2019
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 2/2020
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-019-02957-7

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