Data mining with imbalanced class distributions: concepts and methods
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Summary
This paper reviews recent work in this subject, focusing in concepts and methods to deal with imbalanced data sets.
- Type
- article
- Published
- 2009-01-01
- Cited by
- 67
- References
- 36
- OpenAlex
- https://openalex.org/W77359230
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:16651273
Keywords
Computer science, Class (philosophy), Focus (optics), Machine learning, Artificial intelligence
References
- Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection
- The Foundations of Cost-Sensitive Learning
- The Case against Accuracy Estimation for Comparing Induction Algorithms
- Balancing Training Data for Automated Annotation of Keywords: a Case Study
- AdaCost: Misclassification Cost-Sensitive Boosting
- Two Modifications of CNN
- Encyclopedia of Machine Learning
- Comparing classifiers when the misallocation costs are uncertain
- A study of the behavior of several methods for balancing machine learning training data
- Extreme re-balancing for SVMs: a case study
- Decision trees with minimal costs
- Learning from imbalanced data in surveillance of nosocomial infection
- Minority report in fraud detection: classification of skewed data
- On the boosting ability of top-down decision tree learning algorithms
- MetaCost: a general method for making classifiers cost-sensitive
- Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm
- Asymptotic Properties of Nearest Neighbor Rules Using Edited Data
- Cost-Sensitive Learning and the Class Imbalance Problem
- Cost-sensitive learning by cost-proportionate example weighting
- Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
Cited by
- Data mining approaches to predict final grade by overcoming class imbalance problem
- Type 2 Diabetes Mellitus Screening and Risk Factors Using Decision Tree: Results of Data Mining
- Tackling the Problem of Data Imbalancing for Melanoma Classification
- Stroke risk prediction model based on demographic data
- A New Under-Sampling Method Using Genetic Algorithm for Imbalanced Data Classification
- A Selective Dynamic Sampling Back-Propagation Approach for Handling the Two-Class Imbalance Problem
- Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
- Identification and Use of PSD-derived Features for the Contextual Detection and Classification of EEG Epileptiform Transients
- An improved dynamic sampling back-propagation algorithm based on mean square error to face the multi-class imbalance problem
- Combining Over-Sampling and Under-Sampling Techniques for Imbalance Dataset
- Ensemble-based supervised learning for predicting diabetes onset
- Fresh Brain Tissue Diagnostics Using Raman Spectroscopy in Humans
- The use of clinical, behavioral, and social determinants of health to improve identification of patients in need of advanced care for depression
- Automated Change Detection in Satellite Imagery: ACDet
- Using Machine Learning Methods to Predict Bias in Nuclear Criticality Safety
- Enhancing solar flare forecasting: a multi-class and multi-label classification approach to handle imbalanced time series
- WebRTC Quality Control in Contextual Communication Systems
- Predicting review helpfulness : the case of class imbalance
- Uncertainty Based Under-Sampling for Learning Naive Bayes Classifiers Under Imbalanced Data Sets
- A simple model for glioma grading based on texture analysis applied to conventional brain MRI
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