Novelty detection algorithm for data streams multi-class problems

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

Faria, ER (Author) Other; João Gama (Author) FEP; Carvalho, APLF (Author) Other

Conference proceedings International

Title: Proceedings of the ACM Symposium on Applied Computing

Pages: 795-800

28th Annual ACM Symposium on Applied Computing, SAC 2013

Coimbra, 18 March 2013 through 22 March 2013

Indexing

Scopus - 51 Citations

Other information

Authenticus ID: P-008-B1K

DOI: 10.1145/2480362.2480515

Abstract (EN): Novelty detection has been presented in the literature as one-class problem. In this case, new examples are classified as either belonging to the target class or not. The examples not explained by the model are detected as belonging to a class named novelty. However, novelty detection is much more general, especially in data streams scenarios, where the number of classes might be unknown before learning and new classes can appear any time. In this case, the novelty concept is composed by different classes. This work presents a new algorithm to address novelty detection in data streams multi-class problems, the MINAS algorithm. Moreover, we also present a new experimental methodology to evaluate novelty detection methods in multi-class problems. The data used in the experiments include artificial and real data sets. Experimental results show that MINAS is able to discover novelties in multi-class problems. Copyright 2013 ACM.

Language: English

Type (Professor's evaluation): Scientific