Adaptive model rules from data streams

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

Authors

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

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 480-492

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013

Prague, 23 September 2013 through 27 September 2013

Indexing

ISI Web of Science

Scopus - 40 Citations

Scientific classification

FOS: Natural sciences

CORDIS: Physical sciences > Computer science

Other information

Authenticus ID: P-008-FNV

DOI: 10.1007/978-3-642-40988-2_31

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 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 system with other streaming regression algorithms. © 2013 Springer-Verlag.

Language: English

Type (Professor's evaluation): Scientific