Abstract: Most ensemble learning methods have high computational complexity, excessive base classifiers and unsatisfactory classification accuracy in case of large-scale data sets. This paper proposes an ensemble pruning algorithm based on frequent patterns. Using the theory of frequent patterns mining, the method
maps the un-pruned ensemble classifier and corresponding sample space to a transactional database, and stores the corresponding classification results in a boolean matrix. After extracting frequent base classifiers from the Boolean matrix and composing a pruning ensemble, the algorithm gives the final pruning ensemble.Experimental results show that this algorithm reduces the number of base classifiers, improves classification accuracy and increases classification efficiency compared with ensemble algorithms of Bagging, AdaBoost, WAVE and RFW.

Key words: large-scale data set, frequent pattern, ensemble pruning, transactional database, Boolean matrix