Case Study on Enhanced Ensemble Learning Technique With Video Network Trafﬁc Forecasting:
Ensemble learning technique attracted much attention in the past few years. Instead of using a single predictor, this approach utilizes a number of diverse accurate predictors to do the job. Many methods have been proposed to build such accurate diverse ensembles, of which bagging and boosting were the most popular. Another method, called Feature Subset Ensembles (FSE), is thoroughly investigated in this work.
This technique builds ensembles by assigning each individual predictor in the ensemble a distinct feature subset from the pool of available features. Extensive comparisons are carried out to compare FSE with other approaches. In addition, several novel variations to the basic FSE are added. The introduced FSE variants outperformed the other methods. Experiments were carried out using three different predictor types: least square error (LSE), artiﬁcial neural networks (ANN), and CART regression trees..
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