A CMAC-based scheme for determining membership with classification of text strings. Issue 7 (October 2016)

• Record Type: Journal Article
• Authors: Ma, Heng; Tseng, Ying-Chih; Chen, Lu-I.
• Is Part Of: Neural computing & applications. Volume 27:Issue 7(2016)
• Page Start: 1959
• Page End: 1967
• Publication Date: 2016-10
• Subjects: Membership determination -- Classification -- Text string -- Neural network; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1989-6
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract Membership determination of text strings has been an important procedure for analyzing textual data of a tremendous amount, especially when time is a crucial factor. Bloom filter has been a well-known approach for dealing with such a problem because of its succinct structure and simple determination procedure. As determination of membership with classification is becoming increasingly desirable, parallel Bloom filters are often implemented for facilitating the additional classification requirement. The parallel Bloom filters, however, tend to produce additional false-positive errors since membership determination must be performed on each of the parallel layers. We propose a scheme based on CMAC, a neural network mapping, which only requires a single-layer calculation to simultaneously obtain information of both the membership and classification. A hash function specifically designed for text strings is also proposed. The proposed scheme could effectively reduce false-positive errors by converging the range of membership acceptance to the minimum for each class during the neural network mapping. Simulation results show that the proposed scheme committed significantly less errors than the benchmark, parallel Bloom filters, with limited and identical memory usage at different classification levels.