Journal Article10.1016/J.PATCOG.2018.03.008
Handling data irregularities in classification: Foundations, trends, and future challenges
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TL;DR: This article provides a bird's eye view of data irregularities, beginning with a taxonomy and characterization of various distribution-based and feature-based irregularities, and discusses the notable and recent approaches that have been taken to make the existing stand-alone as well as ensemble classifiers robust against such irregularities.
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About: This article is published in Pattern Recognition. The article was published on 01 Sep 2018.
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Citations
Generative Adversarial Minority Oversampling
Sankha Subhra Mullick,Shounak Datta,Swagatam Das +2 more
- 22 Mar 2019
TL;DR: In this article, a three-player adversarial game between a convex generator, a multi-class classifier network, and a real/fake discriminator is proposed to perform oversampling in deep learning systems.
Neighbourhood-based undersampling approach for handling imbalanced and overlapped data
TL;DR: Four methods based on neighbourhood searching with different criteria to identify potential overlapped instances are proposed in this paper and show comparable performance with state-of-the-art methods across different common metrics with exceptional and statistically significant improvements in sensitivity.
243
On the class overlap problem in imbalanced data classification.
TL;DR: Critical discussion and objective evaluation of class overlap in the context of imbalanced data and its impact on classification accuracy and an in-depth critical technical review of existing approaches to handle imbalanced datasets are provided.
A survey of swarm and evolutionary computing approaches for deep learning
TL;DR: A comprehensive survey of the most recent approaches involving the hybridization of SI and EC algorithms for DL, the architecture of DNNs, and DNN training to improve the classification accuracy is presented.
180
Enabling Smart Data: Noise filtering in Big Data classification
TL;DR: In this article, two Big Data preprocessing approaches to remove noisy examples are proposed: an homogeneous ensemble and an heterogeneous ensemble filter, with special emphasis in their scalability and performance traits.
151
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