Title: An efficient approximation to lookahead in relational learners
Authors: Struyf, Jan ×
Davis, Jesse
Page, David #
Issue Date: 2006
Publisher: Springer
Series Title: Lecture Notes in Computer Science vol:4212 pages:775-782
Conference: 17th European Conference on Machine Learning location:Berlin, Germany date:September 18-22, 2006
Abstract: Greedy machine learning algorithms suffer from shortsightedness, potentially returning suboptimal models due to limited exploration of the search space. Greedy search misses useful refinements that yield a significant gain only in conjunction with other conditions. Relational learners, such as inductive logic programming algorithms, are especially susceptible to this problem. Lookahead helps greedy search overcome myopia; unfortunately it causes an exponential increase in execution time. Furthermore, it may lead to overfitting. We propose a heuristic for greedy relational learning algorithms that can be seen as an efficient, limited form of lookahead. Our experimental evaluation shows that the proposed heuristic yields models that are as accurate as models generated using lookahead. It is also considerably faster than lookahead.
ISBN: 978-3-540-45375-8
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

Files in This Item:
File Status SizeFormat
42267.pdf Published 154KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science