An ensemble classification approach for handling spatio-temporal drifts in partially labeled data streams

Abstract

The classification of streaming data requires learning in an environment where the distribution of the incoming data might change continuously. Stream classification methodologies need to adapt to these changes under limitations of time and memory resources. As such, it is not possible to expect all the samples in the stream to be labeled, as labeling is often time consuming and expensive. In this paper a new ensemble classification approach is proposed, which can handle Spatio-Temporal drifts in streams even when the labeling is limited. The proposed methodology uses a grid density clustering approach to track drifts in the spatial configuration of the data, and maintains a set of classifier models local to each cluster, to track its evolution over time. Structured weighted aggregation of the models across all clusters is performed to produce an overall effective prediction on a new sample. Additionally, a uniform sampling approach amenable to the grid representation of the clusters is proposed, which selects samples to be labeled while preserving the grid density information of the stream. This provides for better selection of representative samples to be labeled, for improved drift detection and handling, while maintaining the labeling budget. Experimental comparison with state of the art drift handling systems shows that the proposed methodology is able to give a high classification performance, with a manageable ensemble size and with only 10% of the samples labeled.

Adaptive random forests for evolving data stream classification

“…This evolving data stream learning setting has motivated the development of a multitude of methods for supervised (Oza 2005;Kolter et al 2003;Brzezinski and Stefanowski 2014;Gomes and Enembreck 2014), unsupervised (Guha et al 2000;Ruiz et al 2009; Barddal et al 2015), and more recently semi-supervised learning (Qin et al 2013;Sethi et al 2014;Parker and Khan 2015). Ensemble learners are often preferred when learning from evolving data streams, since they are able to achieve high learning performance, without much optimization, and have the advantageous characteristic of being flexible as new learners can be selectively added, updated, reset or removed (Kolter et al 2003;Bifet et al 2009Brzezinski and Stefanowski 2014).…”

No Free Lunch Theorem for concept drift detection in streaming data classification: A review

“…The detection part of the framework can work without labels. An improved ensemble‐based grid density framework was proposed by Sethi et al () to tackle concept drift in both spatial and temporal component of the data stream. The grid initially maps out the special characteristics of the data space using grid density clustering.…”