ECML/PKDD 2004, Pisa, Italy, September 20-24, 2004
[Introduction] [Program] [Proceedings] [Call for Papers] [Organizer] [Programme Committee]

ECML/PKDD 04 Workshop

Advances in Inductive Rule Learning

Introduction

Rule learning has a long history within the field of machine learning. In particular, the so-called separate-and-conquer or covering family of rule-based classification algorithms goes back to the early days of machine learning. Since the first papers on the AQ rule learning algorithm, this research area has had its ups and downs but it never completely vanished from the research menus of our field. The primary reason for this lies in the attractiveness of rules as the arguably most comprehensive concept representation formalism. After its peak in the early nineties (through the advent of Inductive Logic Programming algorithms), the focus of research in rule learning soon shifted to association rule discovery, and interest in inductive rule learning declined. Lately, we can observe another increase of interest in this area. Recent advances include novel methods for handling contradicting or missing predictions, multi-instance rule learning, subgroup discovery, integration of boosting and covering, covering on association rules, statistical approaches to rule-based prediction and clustering, efficient learning with rule templates, alternatives to the covering strategy improved strategies for handling multi-class problems, meta-learning of rule evaluation metrics, and many more (references to papers on these topics can be found in the call for papers).

The presentations at this workshop represent a snapshot of ongoing work in this area, and touch upon many of the above-mentioned topics. The breadth of problems addressed in these works underlines that despite its long history, rule learning is far from being a well-understood problem. We believe the time was right for a workshop devoted to this ancient topic in machine learning.

The papers presented at this workshop offer a representative snapshot of ongoing work in this area, and touch upon many of the above-mentioned topics. The breadth of problems addressed in these works underlines that despite its long history, rule learning is far from being a well-understood problem. We believe the time was right for a workshop devoted to this ancient topic in machine learning.

Schedule

Each Paper will be allotted approximately 20 min presentation time including discussions. At the end of each session, there is another 10 minutes buffer time / discussion time.

Proceedings

Organizer

Johannes Fürnkranz (fuernkranz@informatik.tu-darmstadt.de)

Knowledge Engineering Group, TU Darmstadt
Hochschulstraße 10, D-64289 Darmstadt, Germany
Phone: +49-6151-166238
Fax: +49-6151-166229

Programme Committee