Data Stream Classification Based on the Gamma Classifier

• Type: Article in International Scientific Journal
• Date: 2015

Authors

Valeria Uriarte Arcia, AV (Author) Other; Lopez Yanez, I (Author) Other; Yanez Marquez, C (Author) Other; João Gama (Author) FEP; Camacho Nieto, O (Author) Other

Journal

Title: Mathematical Problems in Engineering

Vol. 2015

ISSN: 1024-123X

Publisher: Hindawi

Indexing

ISI Web of Knowledge - 13 Citations

Scopus - 16 Citations

Other information

Authenticus ID: P-00G-KYY

DOI: 10.1155/2015/939175

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17