Skip to main content

Active Learning for Classifying Data Streams with Unknown Number of Classes.

Mohamad, S., Sayed-Mouchaweh, M. and Bouchachia, A., 2018. Active Learning for Classifying Data Streams with Unknown Number of Classes. Neural Networks, 98 (February), 1-15.

Full text available as:

NeuralNetworks_Journal.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.


DOI: 10.1016/j.neunet.2017.10.004


The classification of data streams is an interesting but also a challenging problem. A data stream may grow infinitely making it impractical for storage prior to processing and classification. Due to its dynamic nature, the underlying distribution of the data stream may change over time resulting in the so-called concept drift or the possible emergence and fading of classes, known as concept evolution. In addition, acquiring labels of data samples in a stream is admittedly expensive if not infeasible at all. In this paper, we propose a novel stream-based active learning algorithm (SAL) which is capable of coping with both concept drift and concept evolution by adapting the classification model to the dynamic changes in the stream. SAL is the first AL algorithm in the literature to explicitly take account of these concepts. Moreover, using SAL, only labels of samples that are expected to reduce the expected future error are queried. This process is done while tackling the problem of sampling bias so that samples that induce the change (i.e., drifting samples or samples coming from new classes) are queried. To efficiently implement SAL, the paper proposes the application of non parametric Bayesian models allowing to cope with the lack of prior knowledge about the data stream. In particular, Dirichlet mixture models and the stick breaking process are adopted and adapted to meet the requirements of online learning. The empirical results obtained on real-world benchmarks demonstrate the superiority of SAL in terms of classification performance over the state-of-the-art methods using average and average class accuracy.

Item Type:Article
Uncontrolled Keywords:Data Streams; Active Learning; Concept Drift; Concept Evolution; Bayesian Online Learning
Group:Faculty of Science & Technology
ID Code:29869
Deposited By: Symplectic RT2
Deposited On:16 Oct 2017 14:55
Last Modified:14 Mar 2022 14:07


Downloads per month over past year

More statistics for this item...
Repository Staff Only -