Title: Concept Drift Systems

Abstract
The purpose of this presentation is to conduct a review of concept drift with reference to machine learning. A concept is defined as a quantity that needs to be predicted where the concept is unstable and its changes over a certain period of time. Common types of concepts are weather patterns, customer preferences, temperature and behavioural changes. The underlying data distribution that is used in explaining concepts will also be subject to some changes as a result of the unstable nature of concepts. Such changes in the underlying data distribution cause the models built on old data to be inconsistent with the new concept’s data which will lead to the updating of the model. This creates a problem known as concept drift which complicates the task of learning the new model and the new data that makes up the concept. A lot of researchers have designed and implemented many algorithms; therefore, these algorithms must be ranked to facilitate to the users and developers their chooses. After ranking these algorithms, we will try to combine two algorithms to provide the best possible algorithm.