Ensemble Learning For Imbalanced Data Classification Problem

Abstract

Imbalanced data is a kind of information that occurs in real life, such as medical diagnosis in which records of seriously ill patients outnumber by records of healthy ones. These imbalanced data affect the learning performance of algorithms in data mining. The boundary of decision in out of balance data chosen by most standard algorithms of machine learning tends to bias toward the majority class and hence misclassify the minority class. Therefore, we present an approach for dealing with imbalanced data classification problem by applying the decision tree ensemble learning using both bagging and boosting techniques to build models that compensate the misclassification with cost sensitive learning. In this research, we build the model templates from different characteristics of synthetic data. We have chosen an appropriate model template for the real data with different imbalanced rating and overlapping ratio. The results showed that the chosen model template can solve the imbalanced data classification problem efficiently. But there are some model templates that cannot classify correctly when imbalanced rate increases.