Conversion of Imbalanced Data Into A Stream Using SMOTE Algorithm

Machine learning approach has got major importance when distribution of data is unknown. Classification of data from the data set causes some problem when distribution of data is unknown. Characterization of raw data relates to whether the data can take on only discrete values or whether the data is continuous. In real world application data drawn from non-stationary distribution, causes the problem of “concept drift” or “non-stationary learning”. Drifting of dataset is often associated with online learning scenario. The goal of intelligent machine learning algorithms is to be able to address a wide spectrum of real world scenarios, then the need for a general framework for learning from, and adapting to, a nonstationary environment that may introduce imbalanced data can be hardly overstated. This paper focus on imbalanced data that results in unequal representation of classes in a pattern recognition problem. There are typically two types on class in an imbalanced pattern recognition problem, majority (negative) and minority (positive).