The Superiority of the Ensemble Classification Methods: A Comprehensive Review

Silas Nzuva, Lawrence Nderu


The modern technologies, which are characterized by cyber-physical systems and internet of things expose organizations to big data, which in turn can be processed to derive actionable knowledge. Machine learning techniques have vastly been employed in both supervised and unsupervised environments in an effort to develop systems that are capable of making feasible decisions in light of past data. In order to enhance the accuracy of supervised learning algorithms, various classification-based ensemble methods have been developed. Herein, we review the superiority exhibited by ensemble learning algorithms based on the past that has been carried out over the years. Moreover, we proceed to compare and discuss the common classification-based ensemble methods, with an emphasis on the boosting and bagging ensemble-learning models. We conclude by out setting the superiority of the ensemble learning models over individual base learners.

Keywords: Ensemble, supervised learning, Ensemble model, AdaBoost, Bagging, Randomization, Boosting, Strong learner, Weak learner, classifier fusion, classifier selection, Classifier combination.

DOI: 10.7176/JIEA/9-5-05

Publication date: August 31st 2019


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