A two-layer scheme for membership and classification querying
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.
classification information systems neural nets architecture