Improving Classification Accuracy through ensemble technique in Data Mining

Abstract

Data Mining is the study to get the knowledge from the huge data sources. It is a technology with huge potential to help the corporate ventures focus on the most important information in their data warehouses or database, so that it will help in making business decisions. Decision making with data mining is very much complex task. Ensemble technique is one of the common strategies to improve the accuracy of classifier. In general ensemble learning is an effective technology that combines the predictions from multiple base classifiers. Most commonly used ensemble techniques are Bagging and Boosting. Stacking is also one of the techniques, but it is less widely used. In this paper, we are focusing on bagging technique. An experiment is carried out using bagging with different datasets from UCI repository to study the classification accuracy improvement