Very fast decision rules for multi-class problems

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

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

Conference proceedings International

Title: Proceedings of the ACM Symposium on Applied Computing

Pages: 795-800

27th Annual ACM Symposium on Applied Computing, SAC 2012

Trento, 26 March 2012 through 30 March 2012

Indexing

Scopus - 15 Citations

Scientific classification

FOS: Engineering and technology

CORDIS: Technological sciences

Other information

Authenticus ID: P-008-4SP

DOI: 10.1145/2245276.2245431

Abstract (EN): Decision rules are one of the most interpretable and flexible models for data mining prediction tasks. Till now, few works presented online, any-time and one-pass algorithms for learning decision rules in the stream mining scenario. A quite recent algorithm, the Very Fast Decision Rules (VFDR), learns set of rules, where each rule discriminates one class from all the other. In this work we extend the VFDR algorithm by decomposing a multi-class problem into a set of two-class problems and inducing a set of discriminative rules for each binary problem. The proposed algorithm maintains all properties required when learning from stationary data streams: online and any-time classifiers, processing each example once. Moreover, it is able to learn ordered and unordered rule sets. The new approach is evaluated on various real and artificial datasets. The new algorithm improves the performance of the previous version and is competitive with the state-of-the-art decision tree learning method for data streams. © 2012 ACM.

Language: English

Type (Professor's evaluation): Scientific