Machine Learning
ECML '93 Proceedings of the European Conference on Machine Learning
Adaptive Directed Acyclic Graphs for Multiclass Classification
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Evaluation Measures for Multi-class Subgroup Discovery
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Top-down induction of first-order logical decision trees
Artificial Intelligence
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
First-Order Multi-class Subgroup Discovery
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Towards effective tutorial feedback for explanation questions: a dataset and baselines
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Plane-based object categorisation using relational learning
Machine Learning
| Hi-index | 0.00 |
In this paper we investigate the lack of reliability and consistency of those binary rule learners in ILP that employ the one-vs-rest binarisation technique when dealing with multi-class domains. We show that we can learn a simple, consistent and reliable multi-class theory by combining the rules of the multiple one-vs-rest theories into one rule list or set. We experimentally show that our proposed methods produce coherent and accurate rule models from the rules learned by a well known ILP learner Aleph.