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Machine Learning
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Pattern Recognition Letters
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Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Improved Generalization Through Explicit Optimization of Margins
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An introduction to support Vector Machines: and other kernel-based learning methods
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MultiBoosting: A Technique for Combining Boosting and Wagging
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Neural Networks: A Comprehensive Foundation
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Boosting the margin: A new explanation for the effectiveness of voting methods
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ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
Online Ensemble Learning: An Empirical Study
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Error bounds for aggressive and conservative AdaBoost
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Supervised nonlinear dimensionality reduction for visualization and classification
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IEEE Transactions on Neural Networks
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Modular symbiotic adaptive neuro evolution for high dimensionality classificatory problems
Intelligent Decision Technologies
A noise-detection based AdaBoost algorithm for mislabeled data
Pattern Recognition
Breast Cancer Diagnosis Using Optimized Attribute Division in Modular Neural Networks
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A remote sensing image classification method based on extreme learning machine ensemble
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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In this paper we present a new approach for boosting methods for the construction of ensembles of classifiers. The approach is based on using the distribution given by the weighting scheme of boosting to construct a non-linear supervised projection of the original variables, instead of using the weights of the instances to train the next classifier. With this method we construct ensembles that are able to achieve a better generalization error and are more robust to noise presence. It has been proved that AdaBoost method is able to improve the margin of the instances achieved by the ensemble. Moreover, its practical success has been partially explained by this margin maximization property. However, in noisy problems, likely to occur in real-world applications, the maximization of the margin of wrong instances or outliers can lead to poor generalization. We propose an alternative approach, where the distribution of the weights given by the boosting algorithm is used to get a supervised projection. Then, the supervised projection is used to train the next classifier using a uniform distribution of the training instances. The proposed approach is compared with three boosting techniques, namely AdaBoost, GentleBoost and MadaBoost, showing an improved performance on a large set of 55 problems from the UCI Machine Learning Repository, and less sensitiveness to noise in the class labels. The behavior of the proposed algorithm in terms of margin distribution and bias-variance decomposition is also studied.