Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Improved Pairwise Coupling Classification with Correcting Classifiers
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
The Journal of Machine Learning Research
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Effectiveness of error correcting output coding methods in ensemble and monolithic learning machines
Pattern Analysis & Applications
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Using diversity measures for generating error-correcting output codes in classifier ensembles
Pattern Recognition Letters
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
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
An incremental node embedding technique for error correcting output codes
Pattern Recognition
Multi-class Protein Classification Using Adaptive Codes
The Journal of Machine Learning Research
Subclass Problem-Dependent Design for Error-Correcting Output Codes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error Correcting Output Codes Using Genetic Algorithm-Based Decoding
NCM '08 Proceedings of the 2008 Fourth International Conference on Networked Computing and Advanced Information Management - Volume 01
CIXL2: a crossover operator for evolutionary algorithms based on population features
Journal of Artificial Intelligence Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
New results on error correcting output codes of kernel machines
IEEE Transactions on Neural Networks
Efficient classification for multiclass problems using modular neural networks
IEEE Transactions on Neural Networks
A prototype classifier based on gravitational search algorithm
Applied Soft Computing
One-against-all ensemble for multiclass pattern classification
Applied Soft Computing
An enhanced classifier fusion model for classifying biomedical data
International Journal of Computational Vision and Robotics
Efficient pairwise classification using local cross off strategy
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
A subspace approach to error correcting output codes
Pattern Recognition Letters
Enhancing directed binary trees for multi-class classification
Information Sciences: an International Journal
TrueSkill-Based pairwise coupling for multi-class classification
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Multi-metric learning for multi-sensor fusion based classification
Information Fusion
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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One of the most important topics in information fusion is the combination of individual classifiers in multi-classifier systems. We have two different tasks in this area: one is the training and construction of ensembles of classifiers, with each one being able to solve the multiclass problem; the other task is the fusion of binary classifiers, with each one solving a different two-class problem to construct a multiclass classifier. This paper is devoted to the study of several aspects on the fusion process of binary classifiers to obtain a multiclass classifier. In the general case of a classification problem with more than two classes, we are faced with the issue that many algorithms either work better with two-class problems or are specifically designed for two-class problems. In such cases, a binarization method that maps the multiclass problem into several two-class problems must be used. In this task, information fusion plays a central role because of the combination of the prediction of the different binary classifiers into a multiclass classifier. Several issues regarding the way binary learners are trained and combined are raised by this task. Issues such as individual accuracy, diversity, and independence are common to other information fusion tasks such as the construction of ensembles of classifiers. This paper presents a study of the different class binarization methods for the various standard multiclass classification problems that have been proposed while addressing aspects not considered in previous works. We are especially concerned with many of the general assumptions in the field that have not been fully assessed by experimentation. We test the different methods in a large set of real-world problems from the UCI Machine Learning Repository, and we use six different base learners. Our results corroborate some of the previous results present in the literature. Furthermore, we present new results regarding the influence of the base learner on the performance of each method. We also show new results on the behavior of binary testing error and the independence of binary classifiers depending on the coding strategy. Finally, we study the behavior of the methods when the number of classes is high and in the presence of noise.