Nonlinear Boosting Projections for Ensemble Construction
The Journal of Machine Learning Research
Improving multiclass pattern recognition with a co-evolutionary RBFNN
Pattern Recognition Letters
Boosting recombined weak classifiers
Pattern Recognition Letters
Robust boosting algorithm against mislabeling in multiclass problems
Neural Computation
Semi-Supervised Boosting for Multi-Class Classification
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Boosting One-Class Support Vector Machines for Multi-Class Classification
Applied Artificial Intelligence
Using Ensemble-Based Reasoning to Help Experts in Melanoma Diagnosis
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
A multiclass classification method based on decoding of binary classifiers
Neural Computation
Boosting with pairwise constraints
Neurocomputing
A hierarchical classifier with growing neural gas clustering
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Dual-population based coevolutionary algorithm for designing RBFNN with feature selection
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A new ensemble-based cascaded framework for multiclass training with simple weak learners
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Orientation invariant features for multiclass object recognition
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Heterogeneous ensemble for feature drifts in data streams
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Coarse-to-fine multiclass learning and classification for time-critical domains
Pattern Recognition Letters
A theory of multiclass boosting
The Journal of Machine Learning Research
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AdaBoost.M2 is a boosting algorithm designed for multiclass problems with weak base classifiers. The algorithm is designed to minimize a very loose bound on the training error. We propose two alternative boosting algorithms which also minimize bounds on performance measures. These performance measures are not as strongly connected to the expected error as the training error, but the derived bounds are tighter than the bound on the training error of AdaBoost.M2. In experiments the methods have roughly the same performance in minimizing the training and test error rates. The new algorithms have the advantage that the base classifier should minimize the confidence-rated error, whereas for AdaBoost.M2 the base classifier should minimize the pseudo-loss. This makes them more easily applicable to already existing base classifiers. The new algorithms also tend to converge faster than AdaBoost.M2.