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Bicriteria Oversampling for Imbalanced Data Classification
Procedia Computer Science, 2022 The paper proposes bicriteria oversampling strategy for mining imbalanced data. We use two specialized criteria for oversampling -classification potential and distance from the borderline between minority and majority instances. The potential is to be maximized and the distance minimized.
Joanna Jedrzejowicz, Piotr Jedrzejowicz
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A New Big Data Model Using Distributed Cluster-Based Resampling for Class-Imbalance Problem
Applied Computer Systems, 2019 The class imbalance problem, one of the common data irregularities, causes the development of under-represented models. To resolve this issue, the present study proposes a new cluster-based MapReduce design, entitled Distributed Cluster-based Resampling ...
Terzi Duygu Sinanc, Sagiroglu Seref
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Evaluating Misclassifications in Imbalanced Data [PDF]
, 2006 Evaluating classifier performance with ROC curves is popular in the machine learning community. To date, the only method to assess confidence of ROC curves is to construct ROC bands. In the case of severe class imbalance with few instances of the minority class, ROC bands become unreliable.
William Elazmeh +2 more
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IEEE Access, 2022 The imbalanced datasets and their classification has pulled in as a hot research topic over the years. It is used in different fields, for example, security, finance, health, and many others.
Abeer S. Desuky +4 more
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SN Applied Sciences, 2021 Considering the complexities and challenges in the classification of multiclass and imbalanced fault conditions, this study explores the systematic combination of unsupervised and supervised learning by hybridising clustering (CLUST) and optimised multi ...
Albert Buabeng +3 more
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Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data
Sensors, 2019 The Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework, and it can get good classification results on general datasets.
Kewen Li +4 more
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Imbalanced Data Classification Algorithm Based on CSD-ELM [PDF]
Jisuanji gongcheng, 2019 The Extreme Learning Machine(ELM) based on cost-sensitive learning has its advantages in dealing with imbalanced data classification problems.However,it fails to consider the distribution characteristics of samples in different classes and the importance
WANG Dafei, XIE Wujie, DONG Wenhan
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TGT: A Novel Adversarial Guided Oversampling Technique for Handling Imbalanced Datasets
Egyptian Informatics Journal, 2021 With the volume of data increasing exponentially, there is a growing interest in helping people to benefit from their data regardless of its poor quality.
Ayat Mahmoud +3 more
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Clustering and Community Detection with Imbalanced Clusters [PDF]
, 2016 Spectral clustering methods which are frequently used in clustering and community detection applications are sensitive to the specific graph constructions particularly when imbalanced clusters are present. We show that ratio cut (RCut) or normalized cut (
Aksoylar, Cem +2 more
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Local case-control sampling: Efficient subsampling in imbalanced data sets [PDF]
, 2014 For classification problems with significant class imbalance, subsampling can reduce computational costs at the price of inflated variance in estimating model parameters.
Fithian, William, Hastie, Trevor
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