Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Inference for the Generalization Error
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
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Comparing Combination Rules of Pairwise Neural Networks Classifiers
Neural Processing Letters
Artificial Intelligence in Medicine
Optimal Extension of Error Correcting Output Codes
Proceedings of the 2006 conference on Artificial Intelligence Research and Development
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Pattern Recognition Letters
A review on the combination of binary classifiers in multiclass problems
Artificial Intelligence Review
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A Study of Hierarchical and Flat Classification of Proteins
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computers and Electronics in Agriculture
Ensemble methods and model based diagnosis using possible conflicts and system decomposition
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
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
Ensembles of bireducts: towards robust classification and simple representation
FGIT'11 Proceedings of the Third international conference on Future Generation Information Technology
Speaker recognition from encrypted VoIP communications
Digital Investigation: The International Journal of Digital Forensics & Incident Response
Enhancing directed binary trees for multi-class classification
Information Sciences: an International Journal
A unified data mining solution for authorship analysis in anonymous textual communications
Information Sciences: an International Journal
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Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees that recursively split a multi-class classification task into a system of dichotomies and provide a statistically sound way of applying two-class learning algorithms to multi-class problems (assuming these algorithms generate class probability estimates). However, there are usually many candidate trees for a given problem and in the standard approach the choice of a particular tree is based on domain knowledge that may not be available in practice. An alternative is to treat every system of nested dichotomies as equally likely and to form an ensemble classifier based on this assumption. We show that this approach produces more accurate classifications than applying C4.5 and logistic regression directly to multi-class problems. Our results also show that ensembles of nested dichotomies produce more accurate classifiers than pairwise classification if both techniques are used with C4.5, and comparable results for logistic regression. Compared to error-correcting output codes, they are preferable if logistic regression is used, and comparable in the case of C4.5. An additional benefit is that they generate class probability estimates. Consequently they appear to be a good general-purpose method for applying binary classifiers to multi-class problems.