Random rules from data streams

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

Authors

Ezilda Almeida (Author) Other; Petr Kosina (Author) Other; João Gama (Author) FEP

Conference proceedings International

Title: Proceedings of the ACM Symposium on Applied Computing

Pages: 813-814

28th Annual ACM Symposium on Applied Computing, SAC 2013

Coimbra, 18 March 2013 through 22 March 2013

Indexing

Scopus - 10 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science

Other information

Authenticus ID: P-008-B2J

DOI: 10.1145/2480362.2480518

Abstract (EN): Existing works suggest that random inputs and random features produce good results in classification. In this paper we study the problem of generating random rule sets from data streams. One of the most interpretable and flexible models for data stream mining prediction tasks is the Very Fast Decision Rules learner (VFDR). In this work we extend the VFDR algorithm using random rules from data streams. The proposed algorithm generates several sets of rules. Each rule set is associated with a set of Natt attributes. The proposed algorithm maintains all properties required when learning from stationary data streams: online and any-time classification, processing each example once. Copyright 2013 ACM.

Language: English

Type (Professor's evaluation): Scientific