Evolutionary data analysis for the class imbalance problem
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Summary
This paper presents a unique evolutionary computing-based data sampling approach as an effective solution for the class imbalance problem, and demonstrates that Evolutionary Sampling, both with and without learner optimization, performs relatively better than other data sampling techniques.
- Type
- article
- Published
- 2010-01-01
- Cited by
- 17
- References
- 41
- OpenAlex
- https://openalex.org/W1689262186
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:46538105
Keywords
Class (philosophy), Computer science, Mathematics, Artificial intelligence
References
- Imbalanced Training Set Reduction and Feature Selection Through Genetic Optimization
- Ai Application Programming (Charles River Media Programming)
- Intermediate Statistical Methods and Applications: A Computer Package Approach
- Expected Allele Coverage and the Role of Mutation in Genetic Algorithms
- AI Application Programming
- The Case against Accuracy Estimation for Comparing Induction Algorithms
- Efficient and Accurate Parallel Genetic Algorithms
- Class imbalance problem in UCS classifier system: fitness adaptation
- Two Modifications of CNN
- Mining with rarity: a unifying framework
- The class imbalance problem in learning classifier systems: a preliminary study
- Class imbalances versus small disjuncts
- COMPARATIVE EVALUATION OF PATTERN RECOGNITION TECHNIQUES FOR DETECTION OF MICROCALCIFICATIONS IN MAMMOGRAPHY
- UCI Repository of machine learning databases
- Robust Classification for Imprecise Environments
- Asymptotic Properties of Nearest Neighbor Rules Using Edited Data
- Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction
- Experimental perspectives on learning from imbalanced data
- C4.5: Programs for Machine Learning
- SMOTE: Synthetic Minority Over-sampling Technique
Cited by
- Low density separation as a stopping criterion for active learning SVM
- Estimating harmfulness of class imbalance by scatter matrix based class separability measure
- Evolutionary approach for automated component-based decision tree algorithm design
- A hybrid Machine Learning methodology for imbalanced datasets
- An investigation on the feasibility of cross-project defect prediction
- Addressing imbalanced classification with instance generation techniques: IPADE-ID
- On the effectiveness of preprocessing methods when dealing with different levels of class imbalance
- How to evaluate an agent's behavior to infrequent events?—Reliable performance estimation insensitive to class distribution
- An exploratory study about the cross-project defect prediction: Impact of using different classification algorithms and a measure of performance in building predictive models
- Optimization of cluster-based evolutionary undersampling for the artificial neural networks in corporate bankruptcy prediction
- Prediction of Bankruptcy using Big Data Analytics based on Fuzzy c-means Algorithm
- Research on Credit Card Default Prediction Based on k-Means SMOTE and BP Neural Network
- A Survey on Unbalanced Classification: How Can Evolutionary Computation Help?
- Novel embedding model predicting the credit card's default using neural network optimized by harmony search algorithm and vortex search algorithm
- Local neighborhood encodings for imbalanced data classification
- Data complexity and classification accuracy correlation in oversampling algorithms
- Threshold Moving for Online Class Imbalance Learning with Dynamic Evolutionary Cost Vector
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