Enhancing the Performance of Classification Using Super Learning

Abstract

Classification is one of the supervised learning models, and enhancing the performance of a classification model has been a challenging research problem in the fields of machine learning (ML) and data mining. The goal of ML is to produce or build a model that can be used to perform classification. It is important to achieve superior performance of the classification model. Obtaining a better performance is important for almost all fields including healthcare. Researchers have been using different ML techniques to obtain better performance of their models; ensemble techniques are also used to combine multiple base learner models. The ML technique called super learning or stacked-ensemble is an ensemble method that finds the optimal weighted average of diverse learning models. In this paper, we have used super learning or stacked-ensemble achieving better performance on four benchmark data sets that are related to healthcare. Experimental results show that super learning has a better performance compared to the individual base learners and the baseline ensemble.