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A Study on Imbalanced Data Streams
2020Data 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 +2 more
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Learning in imbalanced relational data
2008 19th International Conference on Pattern Recognition, 2008Traditional learning techniques learn from flat data files with the assumption that each class has a similar number of examples. However, the majority of real-world data are stored as relational systems with imbalanced data distribution, where one class of data is over-represented as compared with other classes.
Amal Saleh Ghanem +2 more
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CLASSIFICATION OF IMBALANCED DATA: A REVIEW
International Journal of Pattern Recognition and Artificial Intelligence, 2009Classification 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 +2 more
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Hybrid sampling for imbalanced data
2008 IEEE International Conference on Information Reuse and Integration, 2008Building 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 +2 more
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Classifying Severely Imbalanced Data
2011Learning 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 +3 more
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Data reduction and stacking for imbalanced data classification
Journal of Intelligent & Fuzzy Systems, 2019Class 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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Design efficiency for imbalanced multilevel data
Behavior Research Methods, 2009The importance of accurate estimation and of powerful statistical tests is widely recognized but has rarely been acknowledged in practice in the social and behavioral sciences. This is especially true for estimation and testing when one is dealing with multilevel designs, not least because approximating accuracy and power is more complex due to having ...
Wilfried, Cools +2 more
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A fuzzy classifier for imbalanced and noisy data
2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2005This paper deals with the learning concept in the presence of noise (overlap) and imbalance in the training set. The starting assumption is that recognition of the smaller class is much more important than that of the larger class. A fuzzy classifier capable of achieving this based on the relation between fuzzy sets and probability distributions as ...
Sofia Visa, Anca L. Ralescu
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The Influence of Sampling on Imbalanced Data Classification
2019 8th Brazilian Conference on Intelligent Systems (BRACIS), 2019Classification tasks using imbalanced data are not challenging on their own. When the classes are linearly separable, a regular classification algorithm usually induces predictive models able to distinguish the classes properly. Imbalanced data poses difficulty for the minority class when the training sets have classes overlapping or a complex border ...
Victor H. Barella +2 more
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Introduction to Imbalanced Data
2019An imbalance of sample sizes among class labels makes it difficult to obtain high classification accuracy in many scientific fields, including medical diagnosis, bioinformatics, biology, and fisheries management. This difficulty is referred to as “class imbalance problem” and is considered to be among the 10 most important problems in data mining ...
Osamu Komori, Shinto Eguchi
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