Learning model rules from high-speed data streams

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

Authors

Almeida, E (Author) Other; Ferreira, C (Author) Other; João Gama (Author) FEP

Conference proceedings International

Title: CEUR Workshop Proceedings

Pages: 10-16

3rd Workshop on Ubiquitous Data Mining, UDM 2013 - Co-located with the 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

3 August 2013

Indexing

Scopus - 2 Citations

Other information

Authenticus ID: P-008-KWT

Abstract (EN): Decision rules are one of the most expressive languages for machine learning. In this paper we present Adaptive Model Rules (AMRules), the first streaming rule learning algorithm for regression problems. In AMRules the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of attribute values. Each rule in AMRules uses a Page-Hinkley test to detect changes in the process generating data and react to changes by pruning the rule set. In the experimental section we report the results of AMRules on benchmark regression problems, and compare the performance of our algorithm with other streaming regression algorithms. © 2013 IJCAI.

Language: English

Type (Professor's evaluation): Scientific