The Strength of Weak Learnability
Machine Learning
Boosting a weak learning algorithm by majority
Information and Computation
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Repairing concavities in ROC curves
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A lazy bagging approach to classification
Pattern Recognition
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We propose a novel ensemble learning algorithm called Triskel, which has two interesting features. First, Triskel learns an ensemble of classifiers, each biased to have high precision on instances from a single class (as opposed to, for example, boosting, where the ensemble members are biased to maximise accuracy over a subset of instances from all classes). Second, the ensemble members' voting weights are assigned so that certain pairs of biased classifiers outweigh the rest of the ensemble, if their predictions agree. Our experiments demonstrate that Triskel often outperforms boosting, in terms of both accuracy and training time. We also present an ROC analysis, which shows that Triskel's iterative structure corresponds to a sequence of nested ROC spaces. The analysis predicts that Triskel works best when there are concavities in the ROC curves; this prediction agrees with our empirical results.