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An ensemble learning method with GAN-based sampling and consistency check for anomaly detection of imbalanced data streams with concept drift. [PDF]

PLoS One
Liu Y, Wang S, Sui H, Zhu L.
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Learning from Imbalanced Data

IEEE Transactions on Knowledge and Data Engineering, 2009
With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data
Haibo He
exaly   +2 more sources

IIvotes ensemble for imbalanced data

Intelligent Data Analysis, 2012
In the paper we present IIvotes – a new framework for constructing an ensemble of classifiers from imbalanced data. IIvotes incorporates the SPIDER method for selective data pre-processing into the adaptive Ivotes ensemble. Such an integration is aimed at improving balance between sensitivity and specificity (evaluated by the G-mean measure) for the ...
Jerzy Blaszczynski, Magdalena Deckert, Jerzy Stefanowski, Szymon Wilk   +3 more
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Protein classification with imbalanced data

Proteins: Structure, Function, and Bioinformatics, 2007
AbstractGenerally, protein classification is a multi‐class classification problem and can be reduced to a set of binary classification problems, where one classifier is designed for each class. The proteins in one class are seen as positive examples while those outside the class are seen as negative examples.
Xing-Ming, Zhao, Xin, Li, Luonan, Chen, Kazuyuki, Aihara   +3 more
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Credal Clustering for Imbalanced Data

2021
Traditional evidential clustering tends to build clusters where the number of data for each cluster fairly close to each other. However, it may not be suitable for imbalanced data. This paper proposes a new method, called credal clustering (CClu), to deal with imbalanced data based on the theory of belief functions.
Zuowei Zhang 0001, Zhunga Liu, Kuang Zhou, Arnaud Martin 0001, Yiru Zhang   +4 more
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A Study on Imbalanced Data Streams

2020
Data are growing fast in today’s world and great portion of that is in the form of stream. In many situations, data streams are imbalanced making it difficult to use with classical data mining methods. However, mining these special kinds of streams is one of the most attractive research area.
Ehsan Aminian, Rita P. Ribeiro, João Gama 0001   +2 more
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CLASSIFICATION OF IMBALANCED DATA: A REVIEW

International Journal of Pattern Recognition and Artificial Intelligence, 2009
Classification of data with imbalanced class distribution has encountered a significant drawback of the performance attainable by most standard classifier learning algorithms which assume a relatively balanced class distribution and equal misclassification costs.
Yanmin Sun, Andrew K. C. Wong, Mohamed S. Kamel   +2 more
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Hybrid sampling for imbalanced data

2008 IEEE International Conference on Information Reuse and Integration, 2008
Building a classification model on imbalanced datasets can be a challenging endeavor. Models built on data where examples of one class are greatly outnumbered by examples of the other class(es) tend to sacrifice accuracy with respect to the underrepresented class in favor of maximizing the overall classification rate.
Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse   +2 more
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Classifying Severely Imbalanced Data

2011
Learning from data with severe class imbalance is difficult. Established solutions include: under-sampling, adjusting classification threshold, and using an ensemble. We examine the performance of combining these solutions to balance the sensitivity and specificity for binary classifications, and to reduce the MSE score for probability estimation.
William Klement, Szymon Wilk, Wojtek Michalowski, Stan Matwin   +3 more
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Data reduction and stacking for imbalanced data classification

Journal of Intelligent & Fuzzy Systems, 2019
Class imbalance arises when the number of examples belonging to one class is much greater than the number of examples belonging to another. The discussed approach focuses on combining several techniques including data reduction and stacking with the aim of improving the performance of the machine classification in the case of imbalanced data. The paper
Ireneusz Czarnowski, Piotr Jedrzejowicz
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