Class imbalances versus small disjuncts
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Summary
It is argued that, in order to improve classifier performance, it may be more useful to focus on the small disjuncts problem than it is tofocus on the class imbalance problem, and experiments suggest that the problem is not directly caused by class imbalances, but rather, that class imbalance may yield small disJuncts which will cause degradation.
- Type
- article
- Published
- 2004-06-01
- Cited by
- 750
- References
- 11
- OpenAlex
- https://openalex.org/W2011376672
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:16051569
Keywords
Computer science, Class (philosophy), Focus (optics), Classifier (UML), Artificial intelligence
References
- Concept Learning and the Problem of Small Disjuncts
- Using Unsupervised Learning to Guide Resampling in Imbalanced Data Sets
- Adaptive Fraud Detection
- Machine Learning for the Detection of Oil Spills in Satellite Radar Images
- The class imbalance problem: A systematic study
- A Multiple Resampling Method for Learning from Imbalanced Data Sets
- The Effects of Adding Noise During Backpropagation Training on a Generalization Performance
- A Quantitative Study of Small Disjuncts
- The class imbalance problem: A systematic study
- UCI Repository of Machine Learning Databases
- Heterogeneous Uncertainty Sampling for Supervised Learning
- Learning with Rare Cases and Small Disjuncts
- Concept-Learning in the Presence of Between-Class and Within-Class Imbalances
Cited by
- Imbalanced Class Learning in Epigenetics
- Predicting protein-ATP binding sites from primary sequence through fusing bi-profile sampling of multi-view features
- Machine Learning Methods for High-Dimensional Imbalanced Biomedical Data
- Learning With An Insufficient Supply Of Data Via Knowledge Transfer And Sharing
- Ensemble diversity for class imbalance learning
- Interpreting and Unifying Outlier Scores
- Handling imbalanced datasets: A review
- Active Learning for Word Sense Disambiguation with Methods for Addressing the Class Imbalance Problem
- Re-sampling Approaches for Regression Tasks under Imbalanced Domains
- An effective Weighted Multi-class Least Squares Twin Support Vector Machine for Imbalanced data classification
- Types of minority class examples and their influence on learning classifiers from imbalanced data
- Random Balance: Ensembles of variable priors classifiers for imbalanced data
- Semantic models for machine learning
- Diversity techniques improve the performance of the best imbalance learning ensembles
- Addressing imbalanced data with argument based rule learning
- Classification of Imbalanced Data Using Synthetic Over-Sampling Techniques
- Instance-based ensemble pruning for imbalanced learning
- An Empirical Study of the Classification Performance of Learners on Imbalanced and Noisy Software Quality Data
- Effects of Distance between Classes and Training Datasets Size to the Performance of XCS: Case of Imbalance Datasets
- Issues in data mining: A comprehensive survey
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