Overview of classification algorithms for unbalanced data
Peng Xu · Journal of Chongqing University of Posts and Telecommunications · 2013
Traditional classification methods are based on the assumption that the training sets are well-balanced,however,in real case the data is usually unbalanced,and the classification performance of the traditional classification is always restricted.A detailed overview of domestic and foreign classification algorithms from the data level and algorithm level is provided in this paper.And through simulation experiments to compare the classification performance of a variety of unbalanced classification algorithm on six different data sets,it is found that the improved classification algorithm has varying degrees of improvement for overall performance.The paper concludes with a list of problems which need solving for the development of unbalanced data classification.