Dynamic ensemble selection classification algorithm based on window over imbalanced drift data stream

Data stream classification is an important research direction in the field of data mining, but in many practical applications, it is impossible to collect the complete training set at one time, and the data may be in an imbalanced state and interspersed with concept drift, which will greatly affect the classification performance. To this end, an online dynamic ensemble selection classification algorithm based on window over imbalanced drift data stream (DESW-ID) is proposed. The algorithm employs various balancing measures, first resampling the data stream using Poisson distribution, and if it is in a highly imbalanced state then secondary sampling is performed using a window storing a minority class instances to achieve the current balanced state of the data. To improve the processing efficiency of the algorithm, a classifier selection ensemble is proposed to dynamically adjust the number of classifiers, and the algorithm runs with an ADWIN detector to detect the presence of concept drift. The experimental results show that the proposed algorithm ranks first on average in all five classification performance metrics compared to the state-of-the-art methods. Therefore, the proposed algorithm has better classification performance for imbalanced data streams with concept drift and also improves the operation efficiency of the algorithm.