On the public citation graph

Istacking: An Iterative Stacking Model to Handle Imbalance Classification Problems

https://doi.org/10.2139/ssrn.4803360
0 signed · 59 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/widm.1143openalex-extracted
Generating ensembles of heterogeneous classifiers using Stacked Generalization
inferredcites →https://doi.org/10.1007/978-3-540-39804-2_12openalex-extracted
SMOTEBoost: Improving Prediction of the Minority Class in Boosting
inferredcites →https://doi.org/10.1007/978-3-642-37456-2_27openalex-extracted
ProWSyn: Proximity Weighted Synthetic Oversampling Technique for Imbalanced Data Set Learning
inferredcites →https://doi.org/10.1007/s00500-008-0323-yopenalex-extracted
KEEL: a software tool to assess evolutionary algorithms for data mining problems
inferredcites →https://doi.org/10.1007/s10115-011-0465-6openalex-extracted
SMOTE-RSB *: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory
inferredcites →https://doi.org/10.1016/j.asoc.2018.04.049openalex-extracted
A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data
inferredcites →https://doi.org/10.1016/j.asoc.2019.105662openalex-extracted
An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets
inferredcites →https://doi.org/10.1016/j.cmpb.2017.09.005openalex-extracted
A deep learning-based multi-model ensemble method for cancer prediction
inferredcites →https://doi.org/10.1016/j.dss.2017.11.006openalex-extracted
A synthetic informative minority over-sampling (SIMO) algorithm leveraging support vector machine to enhance learning from imbalanced datasets
inferredcites →https://doi.org/10.1016/j.eswa.2010.06.048openalex-extracted
A comparative assessment of ensemble learning for credit scoring
inferredcites →https://doi.org/10.1016/j.eswa.2015.10.031openalex-extracted
Adaptive semi-unsupervised weighted oversampling (A-SUWO) for imbalanced datasets
inferredcites →https://doi.org/10.1016/j.eswa.2016.12.035openalex-extracted
Learning from class-imbalanced data: Review of methods and applications
inferredcites →https://doi.org/10.1016/j.eswa.2017.03.073openalex-extracted
Self-Organizing Map Oversampling (SOMO) for imbalanced data set learning
inferredcites →https://doi.org/10.1016/j.eswa.2020.113660openalex-extracted
An ensemble imbalanced classification method based on model dynamic selection driven by data partition hybrid sampling
inferredcites →https://doi.org/10.1016/j.eswa.2020.114246openalex-extracted
A Neighborhood Undersampling Stacked Ensemble (NUS-SE) in imbalanced classification
inferredcites →https://doi.org/10.1016/j.inffus.2019.07.006openalex-extracted
Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting
inferredcites →https://doi.org/10.1016/j.ins.2018.06.056openalex-extracted
Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE
inferredcites →https://doi.org/10.1016/j.ins.2020.05.040openalex-extracted
Resampling ensemble model based on data distribution for imbalanced credit risk evaluation in P2P lending
inferredcites →https://doi.org/10.1016/j.ins.2021.12.063openalex-extracted
A machine learning and genetic algorithm-based method for predicting width deviation of hot-rolled strip in steel production systems
inferredcites →https://doi.org/10.1016/j.knosys.2015.04.022openalex-extracted
Random Balance: Ensembles of variable priors classifiers for imbalanced data
inferredcites →https://doi.org/10.1016/j.knosys.2015.11.013openalex-extracted
Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data
inferredcites →https://doi.org/10.1016/j.knosys.2018.02.031openalex-extracted
IMCStacking: Cost-sensitive stacking learning with feature inverse mapping for imbalanced problems
inferredcites →https://doi.org/10.1016/j.knosys.2019.06.022openalex-extracted
SMOTE based class-specific extreme learning machine for imbalanced learning
inferredcites →https://doi.org/10.1016/j.neucom.2020.04.078openalex-extracted
Optimizing Weighted Extreme Learning Machines for imbalanced classification and application to credit card fraud detection
inferredcites →https://doi.org/10.1109/access.2022.3148298openalex-extracted
A Neural Network Ensemble With Feature Engineering for Improved Credit Card Fraud Detection
inferredcites →https://doi.org/10.1109/cidm.2009.4938667openalex-extracted
Diversity analysis on imbalanced data sets by using ensemble models
inferredcites →https://doi.org/10.1109/cidm.2011.5949434openalex-extracted
Local neighbourhood extension of SMOTE for mining imbalanced data
inferredcites →https://doi.org/10.1109/icde48307.2020.00078openalex-extracted
Self-paced Ensemble for Highly Imbalanced Massive Data Classification
inferredcites →https://doi.org/10.1109/ijcnn.2008.4633969openalex-extracted
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
inferredcites →https://doi.org/10.1109/jas.2019.1911447openalex-extracted
An embedded feature selection method for imbalanced data classification
inferredcites →https://doi.org/10.1109/jas.2022.105518openalex-extracted
Stable Label-Specific Features Generation for Multi-Label Learning via Mixture-Based Clustering Ensemble
inferredcites →https://doi.org/10.1109/jas.2022.105752openalex-extracted
A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems
inferredcites →https://doi.org/10.1109/jas.2023.124008openalex-extracted
A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection
inferredcites →https://doi.org/10.1109/jiot.2022.3200964openalex-extracted
Minority-Weighted Graph Neural Network for Imbalanced Node Classification in Social Networks of Internet of People
inferredcites →https://doi.org/10.1109/tase.2016.2594288openalex-extracted
Multifeature, Sparse-Based Approach for Defects Detection and Classification in Semiconductor Units
inferredcites →https://doi.org/10.1109/tcss.2020.3033302openalex-extracted
Aspect-Based Sentiment Analysis: A Survey of Deep Learning Methods
inferredcites →https://doi.org/10.1109/tcyb.2020.3016972openalex-extracted
A Deep-Ensemble-Level-Based Interpretable Takagi–Sugeno–Kang Fuzzy Classifier for Imbalanced Data
inferredcites →https://doi.org/10.1109/tii.2022.3195171openalex-extracted
Deeply Supervised Subspace Learning for Cross-Modal Material Perception of Known and Unknown Objects
inferredcites →https://doi.org/10.1109/tkde.2008.239openalex-extracted
Learning from Imbalanced Data
inferredcites →https://doi.org/10.1109/tkde.2012.232openalex-extracted
MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
inferredcites →https://doi.org/10.1109/tkde.2015.2458858openalex-extracted
To Combat Multi-Class Imbalanced Problems by Means of Over-Sampling Techniques
inferredcites →https://doi.org/10.1109/tkde.2022.3222047openalex-extracted
Locating Multiple Equivalent Feature Subsets in Feature Selection for Imbalanced Classification
inferredcites →https://doi.org/10.1109/tnnls.2017.2755595openalex-extracted
A Distance-Based Weighted Undersampling Scheme for Support Vector Machines and its Application to Imbalanced Classification
inferredcites →https://doi.org/10.1109/tnnls.2022.3177695openalex-extracted
Classifier Ensemble Based on Multiview Optimization for High-Dimensional Imbalanced Data Classification
inferredcites →https://doi.org/10.1109/tsmc.2019.2932436openalex-extracted
A Novel Fuzzy Logic-Based Text Classification Method for Tracking Rare Events on Twitter
inferredcites →https://doi.org/10.1109/tsmc.2023.3335241openalex-extracted
BO-SMOTE: A Novel Bayesian-Optimization-Based Synthetic Minority Oversampling Technique
inferredcites →https://doi.org/10.1109/tsmca.2009.2029559openalex-extracted
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
inferredcites →https://doi.org/10.1109/tsmcb.2008.2007853openalex-extracted
Exploratory Undersampling for Class-Imbalance Learning
inferredcites →https://doi.org/10.1109/tsmcc.2011.2161285openalex-extracted
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
inferredcites →https://doi.org/10.1109/waina.2010.40openalex-extracted
A New Over-Sampling Method Based on Cluster Ensembles
inferredcites →https://doi.org/10.1142/s0218213013500085openalex-extracted
SYNTHETIC OVERSAMPLING OF INSTANCES USING CLUSTERING
inferredcites →https://doi.org/10.1145/1007730.1007735openalex-extracted
A study of the behavior of several methods for balancing machine learning training data
inferredcites →https://doi.org/10.1177/1548512920962219openalex-extracted
Stacked generalizations in imbalanced fraud data sets using resampling methods
inferredcites →https://doi.org/10.1186/1756-0381-6-16openalex-extracted
LVQ-SMOTE – Learning Vector Quantization based Synthetic Minority Over–sampling Technique for biomedical data
inferredcites →https://doi.org/10.1186/s12859-017-1578-zopenalex-extracted
CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests
inferredcites →https://doi.org/10.1613/jair.953openalex-extracted
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-extracted
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.48550/arxiv.2111.12776openalex-extracted
IMBENS: Ensemble Class-imbalanced Learning in Python
inferredcites →https://doi.org/10.5194/hess-25-2543-2021openalex-extracted
Resampling and ensemble techniques for improving ANN-based high-flow forecast accuracy
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.