Detection of evolving concepts in non-stationary data streams: A multiple kernel learning approach

Due to the unprecedented speed and volume of generated raw data in most of applications, data stream mining has attracted a lot of attention recently. Methods for solving these problems should address challenges in this area such as infinite length, concept-drift, recurring concepts, and concept-evolution. Moreover, due to the speedy intrinsic of data streams, the time and space complexity of the methods are extremely important. This paper proposes a novel method based on multiple-kernels for classifying non-stationary data streams, which addresses the mentioned challenges with special attention to the space complexity. By learning multiple kernels and specifying the boundaries of classes in the feature (mapped) space of combined kernels, the required amount of memory will be decreased, These kernels will be updated regularly throughout the stream when the true labels of instances are received. Newly arrived instances will be classified with respect to their distance to boundaries of the previously known classes in the feature spaces. Due to the efficient memory usage, the computation time does not increase significantly through the stream. We evaluate the performance of the proposed method using a set of experiments conducted on both real and synthetic benchmark data sets. The experimental results show the superiority of the proposed method over the state-of-the-art methods in this area. (C) 2017 Elsevier Ltd. All rights reserved.