An integrated approach to learning Bayesian networks of rules

• Type: Article in International Conference Proceedings Book
• Date: 2005

Authors

Davis, J (Author) Other; Burnside, E (Author) Other; De Castro Dutra, I (Author) Other; Page, D (Author) Other; Santos Costa, V (Author) FCUP

Conference proceedings International

Title: Machine Learning: ECML 2005

Pages: 84-95

16th European Conference on Machine Learning - ECML 2005

Porto, Portugal, October 3-7, 2005

Indexing

ISI Web of Knowledge - 21 Citations

Scopus - 44 Citations

Other information

Authenticus ID: P-007-FH1

DOI: 10.1007/11564096_13

Abstract (EN): Inductive Logic Programming (ILP) is a popular approach for learning rules for classification tasks. An important question is how to combine the individual rules to obtain a useful classifier. In some instances, converting each learned rule into a binary feature for a Bayes net learner improves the accuracy compared to the standard decision list approach [3,4,14]. This results in a two-step process, where rules are generated in the first phase, and the classifier is learned in the second phase. We propose an algorithm that interleaves the two steps, by incrementally building a Bayes net during rule learning. Each candidate rule is introduced into the network, and scored by whether it improves the performance of the classifier. We call the algorithm SAYU for Score As You Use. We evaluate two structure learning algorithms Naïve Bayes and Tree Augmented Naïve Bayes. We test SAYU on four different datasets and see a significant improvement in two out of the four applications. Furthermore, the theories that SAYU learns tend to consist of far fewer rules than the theories in the two-step approach. © Springer-Verlag Berlin Heidelberg 2005.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12