Ensemble Learning with Biased Classifiers: The Triskel Algorithm

We propose a novel ensemble learning algorithm called Triskel, which has two interesting features. First, Triskel learns an ensemble of classifiers that are biased to have high precision (as opposed to, for example, boosting, where the ensemble members are biased to ignore portions of the instance space). Second, Triskel uses weighted voting like most ensemble methods, but the weights are assigned so that certain pairs of biased classifiers outweigh the rest of the ensemble, if their predictions agree. Our experiments on a variety of real-world tasks demonstrate that Triskel often outperforms boosting, in terms of both accuracy and training time.