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
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Ensembles of nested dichotomies for multi-class problems
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Improving Multiclass Pattern Recognition by the Combination of Two Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise classifier combination using belief functions
Pattern Recognition Letters
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Ensembles of balanced nested dichotomies for multi-class problems
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Efficient classification for multiclass problems using modular neural networks
IEEE Transactions on Neural Networks
An experimental study on ensembles of functional trees
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
A random forest classifier for lymph diseases
Computer Methods and Programs in Biomedicine
| Hi-index | 0.10 |
Ensemble methods are often able to generate more accurate classifiers than the individual classifiers. In multiclass problems, it is possible to obtain an ensemble combining binary classifiers. It is sensible to use a multiclass method for constructing the binary classifiers, because the ensemble of binary classifiers can be more accurate than the individual multiclass classifier. Ensemble of nested dichotomies (END) is a method for dealing with multiclass classification problems using binary classifiers. A nested dichotomy organizes the classes in a tree, each internal node has a binary classifier. A set of classes can be organized in different ways in a nested dichotomy. An END is formed by several nested dichotomies. This paper studies the use of this method in conjunction with ensembles of decision trees (forests). Although forests methods are able to deal directly with several classes, their accuracies can be improved if they are used as base classifiers for ensembles of nested dichotomies. Moreover, the accuracies can be improved even more using forests of nested dichotomies, that is, ensemble methods that use as base classifiers a nested dichotomy of decision trees. The improvements over forests methods can be explained by the increased diversity of the base classifiers. The best overall results were obtained using MultiBoost with resampling.