A survey on data preprocessing for data stream mining

TL;DR: A high quality, instructive review of current research developments and trends in the concept drift field is conducted, and a framework of learning under concept drift is established including three main components: concept drift detection, concept drift understanding, and concept drift adaptation.

TL;DR: In this paper, the authors present a review of the recent research in the field of concept drift and propose a framework of learning under concept drift. But, the focus of this survey is on the detection, understanding and adaptation of the concept drift in streaming data.

TL;DR: Incremental learning, online learning, and data stream learning are terms commonly associated with learning algorithms that update their models given a continuous influx of data without performing any act of reinforcement learning.

TL;DR: In this article, the authors present a comprehensive overview of the use of Spiking Neural Networks for online learning in non-stationary data streams and propose a new algorithm to adapt to these changes as fast as possible, while maintaining good performance scores.

TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.

TL;DR: This chapter discusses the properties of Population Principal Components, and the role of Principal Components in Regression Analysis, and discusses generalizations and Adaptations of Principal Component Analysis.

TL;DR: In this article, a new representation learning approach for domain adaptation is proposed, in which data at training and test time come from similar but different distributions, and features that cannot discriminate between the training (source) and test (target) domains are used to promote the emergence of features that are discriminative for the main learning task on the source domain.

TL;DR: A new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions, which can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer.