TY - JOUR
T1 - Data stream classification based on the gamma classifier
AU - Uriarte-Arcia, Abril Valeria
AU - López-Yáñez, Itzamá
AU - Yáñez-Márquez, Cornelio
AU - Gama, João
AU - Camacho-Nieto, Oscar
N1 - Publisher Copyright:
© 2015 Abril Valeria Uriarte-Arcia et al.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84940842045&partnerID=8YFLogxK
U2 - 10.1155/2015/939175
DO - 10.1155/2015/939175
M3 - Artículo
SN - 1024-123X
VL - 2015
JO - Mathematical Problems in Engineering
JF - Mathematical Problems in Engineering
M1 - 939175
ER -