Data Selection for Neural Networks

Mirosław Kordos

Titles

Abstract

Several approaches to joined feature and instance selection in neural network leaning are discussed and experimentally evaluated in respect to classification accuracy and dataset compression, considering also their computational complexity. These include various versions of feature and instance selection prior to the network learning, the selection embedded in the neural network and hybrid approaches, including solutions developed by us. The advantages and disadvantages of each approach are discussed and some possible improvements are proposed.

Keywords

Neural Networks, Data Selection, Feature Selection, Instance Selection

References

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Information

Information: Schedae Informaticae, 2016, Volume 25, pp. 153 - 164

DOI: https://doi.org/10.4467/20838476SI.16.012.6193

Article type: Original article

Titles:

Polish:

Data Selection for Neural Networks

English:

Data Selection for Neural Networks

Authors

Mirosław Kordos

University of Bielsko-Biala Department of Computer Science

Published at: 24.03.2017

Article status: Open

Licence: None

Percentage share of authors:

Mirosław Kordos (Author) - 100%

Article corrections:

-

Publication languages:

English

View count: 2107

Number of downloads: 4992

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References

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Authors