Ensemble Method to classify multi class with concept drift

Abstract

Real world scenarios is expected to have more number of classes, evolution of new class, change in concept, different distribution of data etc., This paper focus on the joint problem of multi class imbalance problem and different types of concept drift detection. The proposed work Enhanced Concept drift based Resampling ensemble (ECDRE) algorithm addresses the combined issues of Multi Class Imbalance problem with different types of Concept Drifts using the Ensemble Relearning Model. In this framework, EMUOB and EMOOB algorithm helps to re-balance the multi class distribution of the latest block. Window based strategy is used to classify the multi class imbalance dataset with different types of drifts. A group of classifiers form an ensemble and members belonging to the ensembles are updated frequently, where weights are calculated to emphasis misclassification cost. Meanwhile, ECDRE is swiftly adaptable to new conditions. Observed studies proof that ECDRE algorithm is more effective for learning non-stationary environment which suffers from multi class imbalance with different types of concept drift. CDRE algorithm was implemented to deal with multi-class problem and concept drift, but it is not much efficient to handle all types of concept drift. Even in situations of Data Streams which suffer from drifts such as real, virtual and recurring types, ECDRE works well and shows overall better results of above 80% accuracy.