A fast adaptive classification approach using kernel ridge regression and clustering for non-stationary data stream

Abstract

Classification on non-stationary data requires faster evolving of the model while keeping the accuracy levels consistent. We present here a faster and reliable model to handle non-stationary data when a small number of labelled samples are available with the stream of unlabelled samples. An active learning model is proposed with the help of supervised model, i.e. Kernel Ridge Regression (KRR) with the combination of an unsupervised model, i.e. K-means clustering to handle the concept drift in the data efficiently. Proposed model consumes less time and at the same time yields similar or better accuracy compared to the existing clustering-based active learning methods. © Springer Nature Singapore Pte Ltd 2019.