Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
Back propagation is sensitive to initial conditions
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
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Machine Learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Pairwise classification and support vector machines
Advances in kernel methods
Data preparation for data mining
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Classification by pairwise coupling
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Data mining: practical machine learning tools and techniques with Java implementations
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Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
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Improved Pairwise Coupling Classification with Correcting Classifiers
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Pairwise Classification as an Ensemble Technique
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ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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An Evaluation of Grading Classifiers
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Winning the KDD99 classification cup: bagged boosting
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Speaker identification via support vector classifiers
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Solving multiclass learning problems via error-correcting output codes
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AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Efficient classification for multiclass problems using modular neural networks
IEEE Transactions on Neural Networks
Decision-tree instance-space decomposition with grouped gain-ratio
Information Sciences: an International Journal
Label ranking by learning pairwise preferences
Artificial Intelligence
Multilabel classification via calibrated label ranking
Machine Learning
On Pairwise Naive Bayes Classifiers
ECML '07 Proceedings of the 18th European conference on Machine Learning
Efficient Pairwise Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Binary Decomposition Methods for Multipartite Ranking
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
On predictive accuracy and risk minimization in pairwise label ranking
Journal of Computer and System Sciences
FR3: a fuzzy rule learner for inducing reliable classifiers
IEEE Transactions on Fuzzy Systems
On exploiting hierarchical label structure with pairwise classifiers
ACM SIGKDD Explorations Newsletter
Learning from label preferences
DS'11 Proceedings of the 14th international conference on Discovery science
Efficient prediction algorithms for binary decomposition techniques
Data Mining and Knowledge Discovery
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In this paper we investigate the performance of pairwise (or round robin) classification, originally a technique for turning multi-class problems into two-class problems, as a general ensemble technique. In particular, we show that the use of round robin ensembles will also increase the classification performance of decision tree learners, even though they can directly handle multi-class problems. The performance gain is not as large as for bagging and boosting, but on the other hand round robin ensembles have a clearly defined semantics. Furthermore, we investigate whether confidence estimates can be used to improve the accuracy of the predictions of the ensemble. Finally, we show that the advantage of pairwise classification over direct multi-class classification and one-against-all binarization increases with the number of classes, and that round robin ensembles form an interesting alternative for problems with ordered class values.