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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 939175, 17 pages
http://dx.doi.org/10.1155/2015/939175
Research Article

Data Stream Classification Based on the Gamma Classifier

1Neural Networks and Unconventional Computing Lab/Alpha-Beta Group, Centro de Investigación en Computación, Instituto Politécnico Nacional, Avenida Juan de Dios Bátiz, Colonia Nuevo Industrial Vallejo, Delegación Gustavo A. Madero, 07738 Mexico City, DF, Mexico
2Intelligent Computing Lab/Alpha-Beta Group, Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Avenida Juan de Dios Bátiz, Colonia Nuevo Industrial Vallejo, Delegación Gustavo A. Madero, 07700 Mexico City, DF, Mexico
3LIAAD-INESC INESC TEC and Faculty of Economics, University of Porto, Rua Dr. Roberto Frias 378, 4200-378 Porto, Portugal

Received 13 April 2015; Revised 7 July 2015; Accepted 29 July 2015

Academic Editor: Konstantinos Karamanos

Copyright © 2015 Abril Valeria Uriarte-Arcia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The ever increasing data generation confronts us with the problem of handling online massive amounts of information. One of the biggest challenges is how to extract valuable information from these massive continuous data streams during single scanning. In a data stream context, data arrive continuously at high speed; therefore the algorithms developed to address this context must be efficient regarding memory and time management and capable of detecting changes over time in the underlying distribution that generated the data. This work describes a novel method for the task of pattern classification over a continuous data stream based on an associative model. The proposed method is based on the Gamma classifier, which is inspired by the Alpha-Beta associative memories, which are both supervised pattern recognition models. The proposed method is capable of handling the space and time constrain inherent to data stream scenarios. The Data Streaming Gamma classifier (DS-Gamma classifier) implements a sliding window approach to provide concept drift detection and a forgetting mechanism. In order to test the classifier, several experiments were performed using different data stream scenarios with real and synthetic data streams. The experimental results show that the method exhibits competitive performance when compared to other state-of-the-art algorithms.