nce the learning process is diversified among base learners by different local subsets of the training data, same type of base learner is used to set up the underlying structure of ensemble system. On the other hand, to promote diversity in learning, it is also possible that different kinds of base learners are trained with the entire training set. The main categories of ensemble learning (boosting, bagging, and voting) are based on both approaches [@zhang2012ensemble].

Bagging systems accommodate multiple homogeneous base learners to cooperate with one another. In a typical bagging system, a subset of the training data is constructed for training each base learner using uniform sampling. Both operations, construction of those subsets and base learners’ being trained with them, are implemented in parallel thanks to the non-existence of direct dependency between subsets or base learners. In general, bagging systems provide a ‘replacement’ option, which enables a data sample to be added to more than one training subset. On the contrary, pasting algorithm [@breiman1999pasting] restricts each data sample to belong to only one training subset. In the testing phase of a bagging system, the majority or average of all predictions made by base learners for testing samples is computed depending on the target problem being classification or regression task respectively [@breiman1996bagging]. Two teams in the competition have deployed this ensemble system with the base learners K-Nearest Neighbor and Support Vector Machines, while one team has opted for a specialized version of bagging system for decision trees, called as Random Forest [@breiman2001random].

Boosting algorithms combine multiple homogeneous base learners to work in collaboration just like bagging systems; however, they define relations and dependencies between those base learners to gradually improve the ensemble performance. In particular, adaptive boosting, a typical instance of boosting methods, trains each base learner with a particular subset of t