Abstract
The goal in Rule Ensemble Learning (REL) is simultaneous discovery of a small set of simple rules and their optimal weights that lead to good generalization. Rules are assumed to be conjunctions of basic propositions concerning the values taken by the input features. It has been shown that rule ensembles for classification can be learnt optimally and efficiently using hierarchical kernel learning approaches that explore the exponentially large space of conjunctions by exploiting its hierarchical structure. The regularizer employed penalizes large features and thereby selects a small set of short features. In this paper, we generalize the rule ensemble learning using hierarchical kernels (RELHKL) framework to multi class structured output spaces. We build on the StructSVM model for sequence prediction problems and employ a ρ-norm hierarchical regularizer for observation features and a conventional 2-norm regularizer for state transition features. The exponentially large feature space is searched using an active set algorithm and the exponentially large set of constraints are handled using a cutting plane algorithm. The approach can be easily extended to other structured output problems. We perform experiments on activity recognition datasets which are prone to noise, sparseness and skewness. We demonstrate that our approach outperforms other approaches.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Proceedings of the AAAI Conference on Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.