On the public citation graph

Biclustering-Knn Joint Learning in Anomaly Detection for Handling Class-Imbalance-Problem

https://doi.org/10.2139/ssrn.4752245
0 signed · 47 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1007/978-3-540-39804-2_12openalex-extracted
SMOTEBoost: Improving Prediction of the Minority Class in Boosting
inferredcites →https://doi.org/10.1007/978-981-16-2102-4_36openalex-extracted
Anomaly Detection: A Survey
inferredcites →https://doi.org/10.1007/s10618-008-0087-0openalex-extracted
Automatically countering imbalance and its empirical relationship to cost
inferredcites →https://doi.org/10.1007/s13748-016-0094-0openalex-extracted
Learning from imbalanced data: open challenges and future directions
inferredcites →https://doi.org/10.1016/j.artmed.2008.08.004openalex-extracted
Dataset complexity in gene expression based cancer classification using ensembles of k-nearest neighbors
inferredcites →https://doi.org/10.1016/j.dim.2023.100064openalex-extracted
Adaptive K-means clustering based under-sampling methods to solve the class imbalance problem
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.2023.122778openalex-extracted
A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation
inferredcites →https://doi.org/10.1016/j.ins.2017.05.008openalex-extracted
Clustering-based undersampling in class-imbalanced data
inferredcites →https://doi.org/10.1016/j.knosys.2011.06.013openalex-extracted
On the effectiveness of preprocessing methods when dealing with different levels of class imbalance
inferredcites →https://doi.org/10.1016/j.neucom.2015.10.042openalex-extracted
A profit-driven Artificial Neural Network (ANN) with applications to fraud detection and direct marketing
inferredcites →https://doi.org/10.1016/j.patcog.2007.04.009openalex-extracted
Cost-sensitive boosting for classification of imbalanced data
inferredcites →https://doi.org/10.1016/j.patcog.2017.12.017openalex-extracted
A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data
inferredcites →https://doi.org/10.1016/j.patrec.2020.05.035openalex-extracted
Imbalance-XGBoost: leveraging weighted and focal losses for binary label-imbalanced classification with XGBoost
inferredcites →https://doi.org/10.1109/access.2021.3081366openalex-extracted
Multiple Filter-Based Rankers to Guide Hybrid Grasshopper Optimization Algorithm and Simulated Annealing for Feature Selection With High Dimensional Multi-Class Imbalanced Datasets
inferredcites →https://doi.org/10.1109/access.2023.3306783openalex-extracted
An Improved AdaBoost Algorithm for Highly Imbalanced Datasets in the Co-Authorship Recommendation Problem
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/fskd.2015.7382311openalex-extracted
A hybrid sampling strategy with optimized Probabilistic Roadmap Method
inferredcites →https://doi.org/10.1109/icarm.2019.8834068openalex-extracted
Bi-Phase evolutionary biclustering algorithm with the NSGA-II algorithm
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/icscan53069.2021.9526422openalex-extracted
Imbalanced Machine Learning Based Techniques for Breast Cancer Detection
inferredcites →https://doi.org/10.1109/icsmd50554.2020.9261679openalex-extracted
Modeling Voice Pathology Detection Using Imbalanced Learning
inferredcites →https://doi.org/10.1109/itaic.2019.8785887openalex-extracted
An Improved Ensemble Learning for Imbalanced Data Classification
inferredcites →https://doi.org/10.1109/nabic.2009.5393690openalex-extracted
Cuckoo Search via Lévy flights
inferredcites →https://doi.org/10.1109/ssci50451.2021.9659937openalex-extracted
Distributed Ensemble Feature Selection Framework for High-Dimensional and High-Skewed Imbalanced Big Dataset
inferredcites →https://doi.org/10.1109/tcyb.2014.2370063openalex-extracted
Biclustering Learning of Trading Rules
inferredcites →https://doi.org/10.1109/tcyb.2016.2579658openalex-extracted
Evolutionary Cluster-Based Synthetic Oversampling Ensemble (ECO-Ensemble) for Imbalance Learning
inferredcites →https://doi.org/10.1109/tcyb.2018.2877663openalex-extracted
Geometric Structural Ensemble Learning for Imbalanced Problems
inferredcites →https://doi.org/10.1109/tevc.2018.2884521openalex-extracted
Bi-Phase Evolutionary Searching for Biclusters in Gene Expression Data
inferredcites →https://doi.org/10.1109/tia.2016.2618756openalex-extracted
Early Fault Detection in Induction Motors Using AdaBoost With Imbalanced Small Data and Optimized Sampling
inferredcites →https://doi.org/10.1109/tii.2021.3094186openalex-extracted
Pareto-Optimal Adaptive Loss Residual Shrinkage Network for Imbalanced Fault Diagnostics of Machines
inferredcites →https://doi.org/10.1109/tip.2017.2781298openalex-extracted
Cost-Sensitive Feature Selection by Optimizing F-Measures
inferredcites →https://doi.org/10.1109/tkde.2006.131openalex-extracted
Test strategies for cost-sensitive decision trees
inferredcites →https://doi.org/10.1109/tkde.2014.2345380openalex-extracted
Resampling-Based Ensemble Methods for Online Class Imbalance Learning
inferredcites →https://doi.org/10.1109/tkde.2020.2995070openalex-extracted
An Effective Imbalanced JPEG Steganalysis Scheme Based on Adaptive Cost-Sensitive Feature Learning
inferredcites →https://doi.org/10.1109/tkde.2022.3171706openalex-extracted
Oversampling with Reliably Expanding Minority Class Regions for Imbalanced Data Learning
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/tnb.2016.2522400openalex-extracted
A Study of Domain Adaptation Classifiers Derived From Logistic Regression for the Task of Splice Site Prediction
inferredcites →https://doi.org/10.1109/tnnls.2015.2430821openalex-extracted
Hybrid Sampling-Based Clustering Ensemble With Global and Local Constitutions
inferredcites →https://doi.org/10.1109/tpami.2011.142openalex-extracted
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study
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/tsmcb.2011.2151256openalex-extracted
Exploiting Local Coherent Patterns for Unsupervised Feature Ranking
inferredcites →https://doi.org/10.1109/tuffc.2021.3110590openalex-extracted
Feature Fusion for Diagnosis of Atypical Hepatocellular Carcinoma in Contrast- Enhanced Ultrasound
inferredcites →https://doi.org/10.1109/tuffc.2021.3132933openalex-extracted
Multi-Task/Single-Task Joint Learning of Ultrasound BI-RADS Features
inferredcites →https://doi.org/10.1142/s1793351x17400050openalex-extracted
Automatic Video Event Detection for Imbalance Data Using Enhanced Ensemble Deep Learning
inferredcites →https://doi.org/10.1162/089976601750264965openalex-extracted
Estimating the Support of a High-Dimensional Distribution
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.