Journal of Computer Engineering & Information TechnologyISSN : 2324-9307

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Research Article, J Comput Eng Inf Technol Vol: 5 Issue: 2

An Effective Framework for Imbalanced Data Stream Classification

Wu K1, Zhang K1*, Fan W2, Gao J3 and Edwards A1

1Department of Computer Science, Xavier University of Louisiana, New Orleans, LA, 70125, USA

2Big Data Lab, Baidu Inc., Sunnyvale, CA, 94089, USA

3University at Buffalo, The State University of New York, 338 Davis Hall, Buffalo, NY, 14260, USA

*Corresponding Author : Kun Zhang
Department of Computer Science, Xavier University of Louisiana, New Orleans, LA, 70125, USA
(504) 520-6700

Received: February 02, 2016 Accepted: June 04, 2016 Published: June 10, 2016

Citation: Wu K, Zhang K, Fan W, Gao J, Edwards A (2016) An Effective Framework for Imbalanced Data Stream Classification. J Comput Eng Inf Technol 5:2. doi: 10.4172/2324-9307.1000148


An Effective Framework for Imbalanced Data Stream Classification

Classifying data streams with skewed distribution finds many applications in realistic environments; however, only a few methods address this joint problem of data stream classification and imbalanced data learning. In this paper, we propose a novel importance sampling driven, dynamic feature group weighting framework (DFGW-IS) to tackle this problem. Our approach addresses the intrinsic characteristics of concept-drifting, imbalanced streaming data. Specifically, the ever-evolving concept is handled by an ensemble trained on a set of feature groups with each sub-classifier (i.e., a single classifier or an ensemble) being weighted by its discriminative power and stable level. The uneven class distribution, on the other hand, is battled by the sub-classifier built in a specific feature group with the underlying distribution rebalanced by the importance sampling technique. We provide the theoretical analysis on the generalization error bound of the proposed algorithm. Extensive experiments on multiple skewed data streams demonstrate that the proposed algorithm not only outperforms the competing methods on standard evaluation metrics, but also adapts well in different learning scenarios.

Keywords: Data stream classification; Class imbalance; Importance sampling; Ensemble weighting; Feature group ensemble

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