Feature Ranking Ensembles for Facial Action Unit Classification
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Ensemble Approaches to Facial Action Unit Classification
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
The Bias Variance Trade-Off in Bootstrapped Error Correcting Output Code Ensembles
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
True Path Rule Hierarchical Ensembles
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Relevance and Redundancy Analysis for Ensemble Classifiers
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Constructing ensembles of classifiers by means of weighted instance selection
IEEE Transactions on Neural Networks
Stopping criteria for ensemble-based feature selection
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
A multiobjective simultaneous learning framework for clustering and classification
IEEE Transactions on Neural Networks
Bootstrap feature selection for ensemble classifiers
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
A comparison of random forest with ECOC-based classifiers
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Correlation-based and causal feature selection analysis for ensemble classifiers
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
Class-Separability weighting and bootstrapping in error correcting output code ensembles
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Low training strength high capacity classifiers for accurate ensembles using walsh coefficients
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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The difficulties of tuning parameters of multilayer perceptrons (MLP) classifiers are well known. In this paper, a measure is described that is capable of predicting the number of classifier training epochs for achieving optimal performance in an ensemble of MLP classifiers. The measure is computed between pairs of patterns on the training data and is based on a spectral representation of a Boolean function. This representation characterizes the mapping from classifier decisions to target label and allows accuracy and diversity to be incorporated within a single measure. Results on many benchmark problems, including the Olivetti Research Laboratory (ORL) face database demonstrate that the measure is well correlated with base-classifier test error, and may be used to predict the optimal number of training epochs. While correlation with ensemble test error is not quite as strong, it is shown in this paper that the measure may be used to predict number of epochs for optimal ensemble performance. Although the technique is only applicable to two-class problems, it is extended here to multiclass through output coding. For the output-coding technique, a random code matrix is shown to give better performance than one-per-class code, even when the base classifier is well-tuned