On the public citation graph

Adaptive ensemble of classifiers with regularization for imbalanced data classification

https://doi.org/10.1016/j.inffus.2020.10.017

0 signed · 99 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1002/0470011815.b2a13003openalex-snapshot

Bagging and Boosting

inferredcites →https://doi.org/10.1007/978-1-4612-0919-5openalex-snapshot

Breakthroughs in Statistics

inferredcites →https://doi.org/10.1007/978-3-642-15387-7_15openalex-snapshot

Nonparametric Statistical Analysis of Machine Learning Algorithms for Regression Problems

inferredcites →https://doi.org/10.1007/s13748-016-0094-0openalex-snapshot

Learning from imbalanced data: open challenges and future directions

inferredcites →https://doi.org/10.1016/0165-1684(84)90013-6openalex-snapshot

Learning characteristics of stochastic-gradient-descent algorithms: A general study, analysis, and critique

inferredcites →https://doi.org/10.1016/j.asoc.2015.08.060openalex-snapshot

Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy

inferredcites →https://doi.org/10.1016/j.eswa.2011.09.059openalex-snapshot

Dynamic classifier ensemble model for customer classification with imbalanced class distribution

inferredcites →https://doi.org/10.1016/j.eswa.2016.12.035openalex-snapshot

Learning from class-imbalanced data: Review of methods and applications

inferredcites →https://doi.org/10.1016/j.eswa.2018.01.008openalex-snapshot

An overlap-sensitive margin classifier for imbalanced and overlapping data

inferredcites →https://doi.org/10.1016/j.inffus.2013.04.006openalex-snapshot

A survey of multiple classifier systems as hybrid systems

inferredcites →https://doi.org/10.1016/j.infsof.2014.12.006openalex-snapshot

ELBlocker: Predicting blocking bugs with ensemble imbalance learning

inferredcites →https://doi.org/10.1016/j.ins.2017.04.044openalex-snapshot

Machine learning based mobile malware detection using highly imbalanced network traffic

inferredcites →https://doi.org/10.1016/j.knosys.2013.01.018openalex-snapshot

Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches

inferredcites →https://doi.org/10.1016/j.knosys.2015.05.027openalex-snapshot

ROSEFW-RF: The winner algorithm for the ECBDL’14 big data competition: An extremely imbalanced big data bioinformatics problem

inferredcites →https://doi.org/10.1016/j.neucom.2013.08.004openalex-snapshot

LibD3C: Ensemble classifiers with a clustering and dynamic selection strategy

inferredcites →https://doi.org/10.1016/j.neucom.2018.01.060openalex-snapshot

A study on combining dynamic selection and data preprocessing for imbalance learning

inferredcites →https://doi.org/10.1016/j.neucom.2018.06.021openalex-snapshot

Combining multiple algorithms in classifier ensembles using generalized mixture functions

inferredcites →https://doi.org/10.1016/j.neunet.2014.09.003openalex-snapshot

Deep learning in neural networks: An overview

inferredcites →https://doi.org/10.1016/j.neunet.2015.06.005openalex-snapshot

Near-Bayesian Support Vector Machines for imbalanced data classification with equal or unequal misclassification costs

inferredcites →https://doi.org/10.1016/j.neunet.2018.05.011openalex-snapshot

Class-specific extreme learning machine for handling binary class imbalance problem

inferredcites →https://doi.org/10.1016/j.neunet.2019.08.018openalex-snapshot

Online sequential class-specific extreme learning machine for binary imbalanced learning

inferredcites →https://doi.org/10.1016/j.patcog.2007.10.012openalex-snapshot

Feature extraction for classification problems and its application to face recognition

inferredcites →https://doi.org/10.1016/j.patcog.2007.10.015openalex-snapshot

From dynamic classifier selection to dynamic ensemble selection

inferredcites →https://doi.org/10.1016/j.patcog.2011.01.017openalex-snapshot

An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes

inferredcites →https://doi.org/10.1016/j.patcog.2014.12.003openalex-snapshot

META-DES: A dynamic ensemble selection framework using meta-learning

inferredcites →https://doi.org/10.1016/j.patcog.2017.10.038openalex-snapshot

A framework for dynamic classifier selection oriented by the classification problem difficulty

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.patcog.2018.05.015openalex-snapshot

Dynamic ensemble selection for multi-class classification with one-class classifiers

inferredcites →https://doi.org/10.1016/j.patrec.2020.05.035openalex-snapshot

Imbalance-XGBoost: leveraging weighted and focal losses for binary label-imbalanced classification with XGBoost

inferredcites →https://doi.org/10.1017/cbo9780511804779openalex-snapshot

Bayesian Reasoning and Machine Learning

inferredcites →https://doi.org/10.1023/a:1008981510081openalex-snapshot

Robust mixture modelling using the t distribution

inferredcites →https://doi.org/10.1080/2150704x.2013.805279openalex-snapshot

Kernel-based extreme learning machine for remote-sensing image classification

inferredcites →https://doi.org/10.1109/34.588027openalex-snapshot

Combination of multiple classifiers using local accuracy estimates

inferredcites →https://doi.org/10.1109/89.365379openalex-snapshot

Robust text-independent speaker identification using Gaussian mixture speaker models

inferredcites →https://doi.org/10.1109/access.2016.2647238openalex-snapshot

A Hybrid Feature Selection With Ensemble Classification for Imbalanced Healthcare Data: A Case Study for Brain Tumor Diagnosis

inferredcites →https://doi.org/10.1109/access.2018.2879221openalex-snapshot

Feature Learning With a Divergence-Encouraging Autoencoder for Imbalanced Data Classification

inferredcites →https://doi.org/10.1109/bracis.2014.14openalex-snapshot

Improving Classifiers and Regions of Competence in Dynamic Ensemble Selection

inferredcites →https://doi.org/10.1109/icmla.2012.212openalex-snapshot

One-Class versus Binary Classification: Which and When?

inferredcites →https://doi.org/10.1109/ictai.2007.49openalex-snapshot

A New Feature Transformation Method Based on Rotation for Speaker Identification

inferredcites →https://doi.org/10.1109/iscisc.2013.6767348openalex-snapshot

A new method for occupational fraud detection in process aware information systems

inferredcites →https://doi.org/10.1109/iscisc21004.2012openalex-snapshot

2012 9th International ISC Conference on Information Security and Cryptology

inferredcites →https://doi.org/10.1109/tdei.2013.6518966openalex-snapshot

Application of fuzzy support vector machine for determining the health index of the insulation system of in-service power transformers

inferredcites →https://doi.org/10.1109/tnnls.2017.2732482openalex-snapshot

Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data

inferredcites →https://doi.org/10.1109/tpami.2018.2858826openalex-snapshot

Focal Loss for Dense Object Detection

inferredcites →https://doi.org/10.1109/tpami.2019.2952096openalex-snapshot

Scalar Quantization as Sparse Least Square Optimization

inferredcites →https://doi.org/10.1109/tsmc.1976.4309523openalex-snapshot

An Experiment with the Edited Nearest-Neighbor Rule

inferredcites →https://doi.org/10.1109/tsmca.2010.2084081openalex-snapshot

Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data

inferredcites →https://doi.org/10.1109/tsmcb.2008.2007853openalex-snapshot

Exploratory Undersampling for Class-Imbalance Learning

inferredcites →https://doi.org/10.1109/tsmcc.2011.2161285openalex-snapshot

A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

inferredcites →https://doi.org/10.1137/16m1080173openalex-snapshot

Optimization Methods for Large-Scale Machine Learning

inferredcites →https://doi.org/10.1142/s0218001419400093openalex-snapshot

Dynamic Ensemble Selection and Data Preprocessing for Multi-Class Imbalance Learning

inferredcites →https://doi.org/10.1145/2108242.2108243openalex-snapshot

Probabilistic ω-automata

inferredcites →https://doi.org/10.1145/2395116.2395117openalex-snapshot

The effectiveness of lloyd-type methods for the k-means problem

inferredcites →https://doi.org/10.1145/2939672.2939785openalex-snapshot

XGBoost

inferredcites →https://doi.org/10.1145/3446776openalex-snapshot

Understanding deep learning (still) requires rethinking generalization

inferredcites →https://doi.org/10.1155/2017/1827016openalex-snapshot

A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM

inferredcites →https://doi.org/10.1162/089976698300017197openalex-snapshot

Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms

inferredcites →https://doi.org/10.1162/neco.1996.8.1.129openalex-snapshot

On Convergence Properties of the EM Algorithm for Gaussian Mixtures

inferredcites →https://doi.org/10.1186/1758-2946-1-21openalex-snapshot

Virtual screening of bioassay data

inferredcites →https://doi.org/10.1214/aos/1013203451openalex-snapshot

Greedy function approximation: A gradient boosting machine.

inferredcites →https://doi.org/10.2307/3001968openalex-snapshot

Individual Comparisons by Ranking Methods

inferredcites →https://doi.org/10.2495/data050031openalex-snapshot

On extending F-measure and G-mean metrics to multi-class problems

inferredcites →https://doi.org/10.25080/majora-92bf1922-011openalex-snapshot

Statsmodels: Econometric and Statistical Modeling with Python

inferredcites →https://doi.org/10.2991/ijcis.10.1.82openalex-snapshot

KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining

inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot

Scikit-learn: Machine Learning in Python

inferredcites →https://doi.org/10.48550/arxiv.1503.08985openalex-snapshot

Iterative Regularization for Learning with Convex Loss Functions

inferredcites →https://doi.org/10.48550/arxiv.1605.08375openalex-snapshot

Generalization Properties and Implicit Regularization for Multiple Passes SGM

inferredcites →https://doi.org/10.48550/arxiv.1608.06048openalex-snapshot

Survey of resampling techniques for improving classification performance in unbalanced datasets

inferredcites →https://doi.org/10.48550/arxiv.1611.06211openalex-snapshot

NoiseOut: A Simple Way to Prune Neural Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4810018openalex-extracted

A Bagging Algorithm for Imprecise Classification in Cost-Sensitive Scenarios

inferred← citeshttps://doi.org/10.1007/978-3-030-79728-7_33openalex-snapshot

Learning Performance Prediction with Imbalanced Virtual Learning Environment Students’ Interactions Data

inferred← citeshttps://doi.org/10.1007/978-981-19-9297-1_26openalex-snapshot

A Classification Method for Imbalanced Data Based on Ant Lion Optimizer

inferred← citeshttps://doi.org/10.1007/978-981-97-9262-7_44openalex-snapshot

Alzheimer’s Disease Identification at Early Stage Using Machine Learning-Based Cognitive Features and Feature Extraction

inferred← citeshttps://doi.org/10.1007/s10489-021-02671-1openalex-snapshot

One-class ensemble classifier for data imbalance problems

inferred← citeshttps://doi.org/10.1007/s11042-023-17186-9openalex-snapshot

An ensemble-based transfer learning model for predicting the imbalance heart sound signal using spectrogram images

inferred← citeshttps://doi.org/10.1007/s11063-021-10671-yopenalex-snapshot

Density Weighted Twin Support Vector Machines for Binary Class Imbalance Learning

inferred← citeshttps://doi.org/10.1007/s11277-023-10687-8openalex-snapshot

Intrusion Attack Detection Using Firefly Optimization Algorithm and Ensemble Classification Model

inferred← citeshttps://doi.org/10.1007/s41060-025-00973-yopenalex-snapshot

Beyond the hypes on data rebalancing in imbalance learning: towards a balanced framework and recommender system

inferred← citeshttps://doi.org/10.1016/j.elerap.2024.101371openalex-snapshot

Ensemble learning with dynamic weighting for response modeling in direct marketing

inferred← citeshttps://doi.org/10.1016/j.engappai.2024.107934openalex-snapshot

An improved generative adversarial network to oversample imbalanced datasets

inferred← citeshttps://doi.org/10.1016/j.engappai.2024.109972openalex-snapshot

Application of deep learning-based multimodal fusion technology in cancer diagnosis: A survey

inferred← citeshttps://doi.org/10.1016/j.future.2024.107655openalex-snapshot

PSPL: A Ponzi scheme smart contracts detection approach via compressed sensing oversampling-based peephole LSTM

inferred← citeshttps://doi.org/10.1016/j.ins.2025.122151openalex-snapshot

A Bagging algorithm for imprecise classification in cost-sensitive scenarios

inferred← citeshttps://doi.org/10.1016/j.ins.2026.123559openalex-snapshot

Boosting algorithm framework for ensemble neural networks based on coordinate descent

inferred← citeshttps://doi.org/10.1016/j.ipm.2022.103235openalex-snapshot

RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification

inferred← citeshttps://doi.org/10.1016/j.jksuci.2024.102253openalex-snapshot

Improving clustering-based and adaptive position-aware interpolation oversampling for imbalanced data classification

inferred← citeshttps://doi.org/10.1016/j.knosys.2021.107248openalex-snapshot

Predicting personalized grouping and consumption: A collaborative evolution model

inferred← citeshttps://doi.org/10.1109/access.2025.3550015openalex-snapshot

Enhancing Cardiovascular Disease Diagnosis: The Power of Optimized Ensemble Learning

inferred← citeshttps://doi.org/10.1109/emr.2022.3230820openalex-snapshot

Early Detection of Alzheimer's Disease Using Cognitive Features: A Voting-Based Ensemble Machine Learning Approach

inferred← citeshttps://doi.org/10.1109/icsec56337.2022.10049365openalex-snapshot

Adaptive Learning Rate For Neural Network Classification Model

inferred← citeshttps://doi.org/10.1155/2021/4953074openalex-snapshot

Cloud Computing Storage Data Access Control Method Based on Dynamic Re-Encryption

inferred← citeshttps://doi.org/10.1155/2022/3429227openalex-snapshot

Situation Element Extraction Based on Fuzzy Rough Set and Combination Classifier

inferred← citeshttps://doi.org/10.1155/2022/7213624openalex-snapshot

Digital Computer Modulation Signal Classification Based on Neural Network

inferred← citeshttps://doi.org/10.2139/ssrn.5546671openalex-snapshot

1 A Fuzzy Membership-Based Oversampling Method with Minority Class

inferred← citeshttps://doi.org/10.2519/josptopen.2024.0718openalex-snapshot

A Systematic Review of Machine Learning Models for Predicting Curve Progression in Teenagers With Idiopathic Scoliosis

inferred← citeshttps://doi.org/10.32604/cmc.2022.025865openalex-snapshot

Imbalanced Classification in Diabetics Using Ensembled Machine Learning

inferred← citeshttps://doi.org/10.3390/agronomy14122940openalex-snapshot

Automated Seedling Contour Determination and Segmentation Using Support Vector Machine and Image Features

inferred← citeshttps://doi.org/10.3390/su14148575openalex-snapshot

Machine-Learning-Based System for the Detection of Entanglement in Dyeing and Finishing Processes

inferred← citeshttps://doi.org/10.7189/jogh.15.04079openalex-snapshot

An ensemble approach improves the prediction of the COVID-19 pandemic in South Korea

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.