An Experiment to Improve Classification Accuracy Using Ensemble Methods

Abstract

Data mining is the practice of analyzing huge quantities of data and shortening it into constructive knowledge. Data Mining is an eternal process which is quite useful in finding understandable patterns and relationships amongst the data. There are various classification techniques available. It is observed that all the techniques don't work well with all datasets. It is found that when the classifiers are used alone, they are not performing as good as when they are combined using ensembles. Ensemble methods are renowned techniques in order to improve the classification accuracy. Bagging and Boosting are the most common ensemble learning techniques used to improve the classification accuracy. Here, a study on the classification accuracy improvement is carried out in which an experiment is performed using boosting with different datasets from UCI repository.