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..
Click here to read more on Enhanced Ensemble Learning Technique..
Related Case Studies:
- Incorporating Flexibility in Manufacturing and Operations – A Case Study in the Indian Context Bharat Heavy Electricals Limited (BHEL), which is one of the...
- Case Study in Logistics Management, India The competitive drivers of the 21st century are time and...
- Case Study on Good Practice Workforce Strategies Introduction: This Good Practice Workforce Strategies Case Studies guide has...
- Case Study on Operations Management at Southwest Airlines Southwest Airlines (Southwest) is the fourth largest airline in the...
- John Deere : A Benchmarking Success Supply Chain Performance Case Study Business boomed for John Deere Limited (JDL) during the 1990s,...