Improving Classifier Performance Using Feature Selection with Ensemble Learning

Abstract

One of the critical task in data mining is classification. It is very much important in classification to achieve maximum accuracy. In the field of data mining, numerous classifiers are present for the classification task. Each classification techniques have their pros and cons. Some of the techniques work well with certain data sets while other techniques work well with other data sets. There have been many techniques evolved for improving classification accuracy. One of such technique is pre-processing which helps in improving quality of the data. Another method is to combine the classifiers, which will in turn improve the classification accuracy. In this paper, empirical study is been done on various techniques for improving classification accuracy. One of the technique is feature selection, which will select best features from the available features in the data set. Other approach is ensemble learning which combines many classifiers to improve the classification accuracy.