Boosting descriptive ILP for predictive learning in bioinformatics

3Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Boosting is an established propositional learning method to promote the predictive accuracy of weak learning algorithms, and has achieved much empirical success. However, there have been relatively few efforts to apply boosting to Inductive Logic Programming (ILP) approaches. We investigate the use of boosting descriptive ILP systems, by proposing a novel algorithm for generating classification rules which searches using a hybrid language bias/production rule approach, and a new method for converting first-order classification rules to binary classifiers, which increases the predictive accuracy of the boosted classifiers. We demonstrate that our boosted approach is competitive with normal ILP systems in experiments with bioinformatics datasets. © Springer-Verlag Berlin Heidelberg 2007.

Cite

CITATION STYLE

APA

Jiang, N., & Colton, S. (2007). Boosting descriptive ILP for predictive learning in bioinformatics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4455 LNAI, pp. 275–289). Springer Verlag. https://doi.org/10.1007/978-3-540-73847-3_28

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free