A theoretical study on classifier ensemble methods and its applications

Ensemble learning is the use of multiple models with a mission to obtain a better predictive efficiency than its constituent models. In order to improvise the efficiency and accuracy of a classifier to find a solution for a given problem/task, ensembles of classifiers has attracted the community of machine learning researchers in the last few decades. Ensemble learning has widely been used for performance improvement in classification. This paper presents a survey on the ensemble methods and its application in various fields.