Neural networks and the bias/variance dilemma
Neural Computation
Applying algebraic and differential invariants for logo recognition
Machine Vision and Applications
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using output codes to boost multiclass learning problems
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
Using diversity measures for generating error-correcting output codes in classifier ensembles
Pattern Recognition Letters
Support vector machine-based image classification for genetic syndrome diagnosis
Pattern Recognition Letters
Stochastic Organization of Output Codes in Multiclass Learning Problems
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECOC-ONE: A Novel Coding and Decoding Strategy
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Gestalt-based feature similarity measure in trademark database
Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Data-driven decomposition for multi-class classification
Pattern Recognition
Subclass Problem-Dependent Design for Error-Correcting Output Codes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass support vector machines for diagnosis of erythemato-squamous diseases
Expert Systems with Applications: An International Journal
Separability of ternary codes for sparse designs of error-correcting output codes
Pattern Recognition Letters
Blurred Shape Model for binary and grey-level symbol recognition
Pattern Recognition Letters
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
On the Decoding Process in Ternary Error-Correcting Output Codes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Thinned-ECOC ensemble based on sequential code shrinking
Expert Systems with Applications: An International Journal
Minimal design of error-correcting output codes
Pattern Recognition Letters
Joint learning of error-correcting output codes and dichotomizers from data
Neural Computing and Applications - Special Issue on ICONIP2010
Evolving Output Codes for Multiclass Problems
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
An AdaBoost Algorithm for Multiclass Semi-supervised Learning
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
Hi-index | 0.01 |
Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design. In this paper, we propose an evolutionary algorithm-based approach to the design of an application-dependent codematrix in the ECOC framework. The central idea of this work is to design a three-dimensional codematrix, where the third dimension is the feature space of the problem domain. In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers. The proposed method takes advantage of some basic concepts of ensemble classification, such as diversity of classifiers, and also benefits from the evolutionary approach for optimizing the three-dimensional codematrix, taking into account the problem domain. We provide a set of experimental results using a set of benchmark datasets from the UCI Machine Learning Repository, as well as two real multiclass Computer Vision problems. Both sets of experiments are conducted using two different base learners: Neural Networks and Decision Trees. The results show that the proposed method increases the classification accuracy in comparison with the state-of-the-art ECOC coding techniques.