Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. This paper explores ensemble methods for unsupervised learning. Here an ensemble comprises multiple clusterers, each of which is trained by k-means algorithm with different initial points. The clusters discovered by different clusterers are aligned, i.e. ...

In this paper, we have tried to go beyond conventional ensemble learning & explore multi-level ensemble learning with reference to recommender systems. In particular, we have focused on stacked generalization for building Movie Recommender System. We have tried to analyze the transition from single level to multi-level ensemble learning and its effects on the overall accuracy. We have used movi...

To maximize the benefit that can be derived from the information implicit in big data, ensemble methods generate multiple models with sufficient diversity through randomization or perturbation. A k-dependence Bayesian classifier (KDB) is a highly scalable learning algorithm with excellent time and space complexity, along with high expressivity. This paper introduces a new ensemble approach of K...

In this paper, the impact of the size of the training set on the benefit from ensemble, i.e. the gains obtained by employing ensemble learning paradigms, is empirically studied. Experiments on Bagged/ Boosted J4.8 decision trees with/without pruning show that enlarging the training set tends to improve the benefit from Boosting but does not significantly impact the benefit from Bagging. This ph...

Ensemble methods for supervised machine learning have become popular due to their ability to accurately predict class labels with groups of simple, lightweight “base learners.” While ensembles offer computationally efficient models that have good predictive capability, they tend to be large and offer little insight into the patterns or structure in a dataset. In this study, we extend an ensembl...