Novelty detection in data streams

• Type: Article in International Scientific Journal
• Date: 2016

Authors

Faria, ER (Author) Other; Goncalves, IJCR (Author) Other; de Carvalho, ACPLF (Author) Other; João Gama (Author) FEP

Journal

Title: Artificial Intelligence Review

Vol. 45 No. 2

Pages: 235-269

ISSN: 0269-2821

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 70 Citations

Scopus - 85 Citations

Other information

Authenticus ID: P-00G-T9F

DOI: 10.1007/s10462-015-9444-8

Abstract (EN): In massive data analysis, data usually come in streams. In the last years, several studies have investigated novelty detection in these data streams. Different approaches have been proposed and validated in many application domains. A review of the main aspects of these studies can provide useful information to improve the performance of existing approaches, allow their adaptation to new applications and help to identify new important issues to be addresses in future studies. This article presents and analyses different aspects of novelty detection in data streams, like the offline and online phases, the number of classes considered at each phase, the use of ensemble versus a single classifier, supervised and unsupervised approaches for the learning task, information used for decision model update, forgetting mechanisms for outdated concepts, concept drift treatment, how to distinguish noise and outliers from novelty concepts, classification strategies for data with unknown label, and how to deal with recurring classes. This article also describes several applications of novelty detection in data streams investigated in the literature and discuss important challenges and future research directions.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 35