Knowledge discovery from infinite data streams is an important and difficult task.We are facing two challenges, the overwhelming volume and the concept drifts of the streaming data. In this chapter, we introduce a general framework for mining concept-drifting data streams using weighted ensemble classifiers. We train an ensemble of classification models, such as C4.5, RIPPER, naive Bayesian, et...

This paper proposes a boosting-like method to train a classifier ensemble from data streams. It naturally adapts to concept drift and allows to quantify the drift in terms of its base learners. The algorithm is empirically shown to outperform learning algorithms that ignore concept drift. It performs no worse than advanced adaptive time window and example selection strategies that store all the...

Abstract Physical systems undergoing spontaneous pattern formation are governed by intrinsic length scales that may compete with extrinsic ones, resulting in exceptional spatiotemporal behaviour. In this work, we report experimental and theoretical evidence spatial nonuniformity sets Faraday-wave patterns motion, which noticeable the zigzag drift dynamics exhibited their wave crests. We provide...

PSR B0818−41 is one of the few pulsars which show multiple drift regions having well defined phase relationship. In this paper we report new results from the multifrequency observations of this pulsar with the Giant Metrewave Radio Telescope (GMRT). We determine the mean flux of the pulsar at multiple frequencies and find evidence for a low frequency turnover in its spectrum. Significant linear...