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CSS : Handling imbalanced data by improved clustering with stratified sampling
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
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Improving Identification of Difficult Small Classes by Balancing Class Distribution
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DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique
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A synthetic neighborhood generation based ensemble learning for the imbalanced data classification
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Dealing with high-dimensional class-imbalanced datasets: Embedded feature selection for SVM classification
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Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection
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Feature selection for high dimensional imbalanced class data using harmony search
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Cluster-based under-sampling approaches for imbalanced data distributions
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Learned lessons in credit card fraud detection from a practitioner perspective
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Learning from class-imbalanced data: Review of methods and applications
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RWO-Sampling: A random walk over-sampling approach to imbalanced data classification
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Adaptive multi-objective swarm fusion for imbalanced data classification
inferredcites →https://doi.org/10.1016/j.ins.2014.07.015openalex-snapshot
Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines
inferredcites →https://doi.org/10.1016/j.ins.2014.08.051openalex-snapshot
SMOTE–IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering
inferredcites →https://doi.org/10.1016/j.ins.2017.05.008openalex-snapshot
Clustering-based undersampling in class-imbalanced data
inferredcites →https://doi.org/10.1016/j.knosys.2018.02.031openalex-snapshot
IMCStacking: Cost-sensitive stacking learning with feature inverse mapping for imbalanced problems
inferredcites →https://doi.org/10.1016/j.knosys.2018.09.007openalex-snapshot
A stratified sampling based clustering algorithm for large-scale data
inferredcites →https://doi.org/10.1016/j.neucom.2016.10.053openalex-snapshot
Large cost-sensitive margin distribution machine for imbalanced data classification
inferredcites →https://doi.org/10.1016/j.neucom.2017.03.011openalex-snapshot
Fast-CBUS: A fast clustering-based undersampling method for addressing the class imbalance problem
inferredcites →https://doi.org/10.1016/j.neucom.2017.06.082openalex-snapshot
Using sub-sampling and ensemble clustering techniques to improve performance of imbalanced classification
inferredcites →https://doi.org/10.1016/j.patcog.2014.11.014openalex-snapshot
A novel ensemble method for classifying imbalanced data
inferredcites →https://doi.org/10.1016/j.patcog.2016.03.012openalex-snapshot
Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets
inferredcites →https://doi.org/10.1016/j.patcog.2017.04.028openalex-snapshot
Class Switching according to Nearest Enemy Distance for learning from highly imbalanced data-sets
inferredcites →https://doi.org/10.1016/j.patcog.2018.03.008openalex-snapshot
Handling data irregularities in classification: Foundations, trends, and future challenges
inferredcites →https://doi.org/10.1016/j.spl.2017.04.026openalex-snapshot
Exact optimal sample allocation: More efficient than Neyman
inferredcites →https://doi.org/10.1109/pdcat46702.2019.00071openalex-snapshot
Imbalanced Data Classification Using Improved Clustering Algorithm and Under-Sampling Method
inferredcites →https://doi.org/10.1109/tcyb.2014.2372060openalex-snapshot
Diversified Sensitivity-Based Undersampling for Imbalance Classification Problems
inferredcites →https://doi.org/10.1109/tcyb.2016.2606104openalex-snapshot
A Noise-Filtered Under-Sampling Scheme for Imbalanced Classification
inferredcites →https://doi.org/10.1109/tit.1968.1054155openalex-snapshot
The condensed nearest neighbor rule (Corresp.)
inferredcites →https://doi.org/10.1109/tkde.2012.232openalex-snapshot
MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
inferredcites →https://doi.org/10.1109/tkde.2014.2324567openalex-snapshot
RACOG and wRACOG: Two Probabilistic Oversampling Techniques
inferredcites →https://doi.org/10.1109/tkde.2015.2458858openalex-snapshot
To Combat Multi-Class Imbalanced Problems by Means of Over-Sampling Techniques
inferredcites →https://doi.org/10.1109/tkde.2017.2682249openalex-snapshot
Confusion-Matrix-Based Kernel Logistic Regression for Imbalanced Data Classification
inferredcites →https://doi.org/10.1109/tnnls.2012.2228231openalex-snapshot
Dynamic Sampling Approach to Training Neural Networks for Multiclass Imbalance Classification
inferredcites →https://doi.org/10.1126/science.1242072openalex-snapshot
Clustering by fast search and find of density peaks
inferredcites →https://doi.org/10.1145/2907070openalex-snapshot
A Survey of Predictive Modeling on Imbalanced Domains
inferredcites →https://doi.org/10.1162/neco.1995.7.1.108openalex-snapshot
Training with Noise is Equivalent to Tikhonov Regularization
inferredcites →https://doi.org/10.1613/jair.953openalex-snapshot
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.2298/csis161221017lopenalex-snapshot
Imbalanced data classification based on hybrid resampling and twin support vector machine
inferredcites →https://doi.org/10.3390/app8050815openalex-snapshot
Class Imbalance Ensemble Learning Based on the Margin Theory
inferredcites →https://doi.org/10.3390/sym10070250openalex-snapshot
A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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inferred← citeshttps://doi.org/10.3390/electronics12214423openalex-snapshot
Stratified Sampling-Based Deep Learning Approach to Increase Prediction Accuracy of Unbalanced Dataset
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A Semantically Enhanced Label Prediction Method for Imbalanced POI Data Category Distribution
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