A two‐layer scheme for membership and classification querying

Heng Ma (Department of Industrial Management, Chung Hua University, Hsinchu, Taiwan)
Hung‐Yu Cheng (PhD Program in Technology Management, Chung Hua University, Hsinchu, Taiwan)

Kybernetes

ISSN: 0368-492X

Publication date: 4 January 2013

Abstract

Purpose

The purpose of this paper is to effectively deal with querying of classification with membership.

Design/methodology/approach

The authors propose a scheme comprising a layer of Bloom filter for membership checking and a second layer based on neural network for dealing with the classification requirement.

Findings

Not only could false positives be dramatically decreased, but also classification could be achieved with the proposed scheme.

Research limitations/implications

The experimental data were randomly generated instead of real‐world ones.

Practical implications

It is difficult to implement this scheme in a real‐world environment, such as the internet. Second, the neural network requires time to converge to a satisfactory level.

Social implications

Internet ethic might be compromised by hackers once they find a way around the filtering mechanism.

Originality/value

The neural network was moditified and utilized for the first time to be suitable for our purpose. Second, the two‐layer design shows effectiveness.

Keywords

Citation

Ma, H. and Cheng, H. (2013), "A two‐layer scheme for membership and classification querying", Kybernetes, Vol. 42 No. 1, pp. 82-93. https://doi.org/10.1108/03684921311295493

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Publisher

:

Emerald Group Publishing Limited

Copyright © 2013, Emerald Group Publishing Limited

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