11 research outputs found
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning
Being able to model correlations between labels is considered crucial in
multi-label classification. Rule-based models enable to expose such
dependencies, e.g., implications, subsumptions, or exclusions, in an
interpretable and human-comprehensible manner. Albeit the number of possible
label combinations increases exponentially with the number of available labels,
it has been shown that rules with multiple labels in their heads, which are a
natural form to model local label dependencies, can be induced efficiently by
exploiting certain properties of rule evaluation measures and pruning the label
search space accordingly. However, experiments have revealed that multi-label
heads are unlikely to be learned by existing methods due to their
restrictiveness. To overcome this limitation, we propose a plug-in approach
that relaxes the search space pruning used by existing methods in order to
introduce a bias towards larger multi-label heads resulting in more expressive
rules. We further demonstrate the effectiveness of our approach empirically and
show that it does not come with drawbacks in terms of training time or
predictive performance.Comment: Preprint version. To appear in Proceedings of the 22nd International
Conference on Discovery Science, 201
Conformal Rule-Based Multi-label Classification
We advocate the use of conformal prediction (CP) to enhance rule-based
multi-label classification (MLC). In particular, we highlight the mutual
benefit of CP and rule learning: Rules have the ability to provide natural
(non-)conformity scores, which are required by CP, while CP suggests a way to
calibrate the assessment of candidate rules, thereby supporting better
predictions and more elaborate decision making. We illustrate the potential
usefulness of calibrated conformity scores in a case study on lazy multi-label
rule learning
Multivariate GARCH models: a survey
This paper surveys the most important developments in multivariate ARCH-type modelling. It reviews the model specifications and inference methods, and identifies likely directions of future research. Copyright © 2006 John Wiley & Sons, Ltd.