An Incremental Learning Algorithm for Non-stationary Environments and Class Imbalance

Abstract

Learning in a non-stationary environment and in the presence of class imbalance has been receiving more recognition from the computational intelligence community, but little work has been done to create an algorithm or a framework that can handle both issues simultaneously. We have recently introduced a new member to the Learn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">++</sup> family of algorithms, Learn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">++</sup> .NSE, which is designed to track non-stationary environments. However, this algorithm does not work well when there is class imbalance as it has not been designed to handle this problem. On the other hand, SMOTE - a popular algorithm that can handle class imbalance - is not designed to learn in nonstationary environments because it is a method of over sampling the data. In this work we describe and present preliminary results for integrating SMOTE and Learn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">++</sup> .NSE to create an algorithm that is robust to learning in a non-stationary environment and under class imbalance.