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Evolutionary computation for training set selection

https://doi.org/10.1002/widm.44
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-31182-3_46openalex-snapshot
Co-evolving Multilayer Perceptrons Along Training Sets
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Prototype Selection and Feature Subset Selection by Estimation of Distribution Algorithms. A Case Study in the Survival of Cirrhotic Patients Treated with TIPS
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A cooperative coevolutionary approach to function optimization
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Text Mining: Predictive Methods for Analyzing Unstructured Information
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Genetic Algorithms + Data Structures = Evolution Programs
inferredcites →https://doi.org/10.1007/bf00114722openalex-snapshot
Efficient reinforcement learning through symbiotic evolution
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Recursive Automatic Bias Selection for Classifier Construction
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Parallel Problem Solving from Nature — PPSN V
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An Approach to Data Reduction and Integrated Machine Classification
inferredcites →https://doi.org/10.1007/s10462-004-4304-yopenalex-snapshot
A Survey of Outlier Detection Methodologies
inferredcites →https://doi.org/10.1007/s10462-010-9165-yopenalex-snapshot
A review of instance selection methods
inferredcites →https://doi.org/10.1007/s10994-009-5161-3openalex-snapshot
A cooperative coevolutionary algorithm for instance selection for instance-based learning
inferredcites →https://doi.org/10.1007/s12293-010-0048-1openalex-snapshot
Stratified prototype selection based on a steady-state memetic algorithm: a study of scalability
inferredcites →https://doi.org/10.1016/0001-8708(77)90069-xopenalex-snapshot
A mathematical theory of evidence
inferredcites →https://doi.org/10.1016/0167-8655(95)00047-kopenalex-snapshot
Editing for the k-nearest neighbors rule by a genetic algorithm
inferredcites →https://doi.org/10.1016/b978-0-08-047865-4.50011-9openalex-snapshot
PARAMETER ESTIMATION
inferredcites →https://doi.org/10.1016/b978-0-08-050684-5.50020-3openalex-snapshot
The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination
inferredcites →https://doi.org/10.1016/j.artint.2004.05.009openalex-snapshot
A selective sampling approach to active feature selection
inferredcites →https://doi.org/10.1016/j.artint.2010.01.001openalex-snapshot
Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts
inferredcites →https://doi.org/10.1016/j.asoc.2009.04.004openalex-snapshot
Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems
inferredcites →https://doi.org/10.1016/j.datak.2006.01.008openalex-snapshot
Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability
inferredcites →https://doi.org/10.1016/j.eswa.2005.10.007openalex-snapshot
Artificial neural networks with evolutionary instance selection for financial forecasting
inferredcites →https://doi.org/10.1016/j.ijmachtools.2005.04.005openalex-snapshot
Effective training data selection in tool condition monitoring system
inferredcites →https://doi.org/10.1016/j.patcog.2007.04.009openalex-snapshot
Cost-sensitive boosting for classification of imbalanced data
inferredcites →https://doi.org/10.1016/j.patcog.2008.02.006openalex-snapshot
A memetic algorithm for evolutionary prototype selection: A scaling up approach
inferredcites →https://doi.org/10.1016/j.patcog.2010.01.006openalex-snapshot
Prototype selection algorithms for distributed learning
inferredcites →https://doi.org/10.1016/j.patrec.2004.09.043openalex-snapshot
Stratification for scaling up evolutionary prototype selection
inferredcites →https://doi.org/10.1016/s0004-3702(00)00004-7openalex-snapshot
Learning to construct knowledge bases from the World Wide Web
inferredcites →https://doi.org/10.1016/s0031-3203(02)00257-1openalex-snapshot
Strategies for learning in class imbalance problems
inferredcites →https://doi.org/10.1016/s0167-9473(99)00095-xopenalex-snapshot
Noisy replication in skewed binary classification
inferredcites →https://doi.org/10.1023/a:1007452223027openalex-snapshot
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
inferredcites →https://doi.org/10.1023/a:1007626913721openalex-snapshot
Reduction Techniques for Instance-Based Learning Algorithms
inferredcites →https://doi.org/10.1023/a:1009876119989openalex-snapshot
A Survey of Methods for Scaling Up Inductive Algorithms
inferredcites →https://doi.org/10.1023/a:1014043630878openalex-snapshot
Advances in Instance Selection for Instance-Based Learning Algorithms
inferredcites →https://doi.org/10.1109/fuzzy.2004.1375451openalex-snapshot
Proceedings - 2004 IEEE International Conference on Fuzzy Systems
inferredcites →https://doi.org/10.1109/tevc.2003.819265openalex-snapshot
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
inferredcites →https://doi.org/10.1109/tevc.2005.844158openalex-snapshot
Cooperative Coevolution of Artificial Neural Network Ensembles for Pattern Classification
inferredcites →https://doi.org/10.1109/tevc.2005.856068openalex-snapshot
An organizational coevolutionary algorithm for classification
inferredcites →https://doi.org/10.1109/tkde.2007.190645openalex-snapshot
Fast Nearest Neighbor Condensation for Large Data Sets Classification
inferredcites →https://doi.org/10.1109/tnn.2003.810618openalex-snapshot
COVNET: a cooperative coevolutionary model for evolving artificial neural networks
inferredcites →https://doi.org/10.1162/106365600568086openalex-snapshot
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
inferredcites →https://doi.org/10.1162/evco.2009.17.3.275openalex-snapshot
Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy
inferredcites →https://doi.org/10.1613/jair.606openalex-snapshot
Identifying Mislabeled Training Data
inferredcites →https://doi.org/10.2333/bhmk.26.145openalex-snapshot
Feature Selection With Neural Networks
inferredcites →https://doi.org/10.5860/choice.27-0936openalex-snapshot
Genetic algorithms in search, optimization, and machine learning
inferredcites →https://doi.org/10.7551/mitpress/1090.001.0001openalex-snapshot
Adaptation in Natural and Artificial Systems

What points at this work

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

inferred← citeshttps://doi.org/10.1002/widm.1087openalex-snapshot
Revisiting evolutionary algorithms in feature selection and nonfuzzy/fuzzy rule based classification
inferred← citeshttps://doi.org/10.1007/978-1-0716-5408-8_2openalex-snapshot
Instance and Feature Selection: Algorithmic Identification of Unique Kinase Protein Interactions from the Protein Data Bank (PDB) and Their Features
inferred← citeshttps://doi.org/10.1007/978-3-030-29349-9_4openalex-snapshot
From Supervised Instance and Feature Selection Algorithms to Dual Selection: A Review
inferred← citeshttps://doi.org/10.1007/978-3-319-46562-3_26openalex-snapshot
A Data Driven Stopping Criterion for Evolutionary Instance Selection
inferred← citeshttps://doi.org/10.1007/s00500-012-0888-3openalex-snapshot
On the use of evolutionary feature selection for improving fuzzy rough set based prototype selection
inferred← citeshttps://doi.org/10.1007/s00521-022-07974-zopenalex-snapshot
Unsupervised instance selection via conjectural hyperrectangles
inferred← citeshttps://doi.org/10.1016/j.asoc.2012.06.013openalex-snapshot
Multi-selection of instances: A straightforward way to improve evolutionary instance selection
inferred← citeshttps://doi.org/10.1016/j.datak.2012.03.002openalex-snapshot
Large scale instance selection by means of federal instance selection
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.128920openalex-snapshot
A comprehensive review on data-level methods for imbalanced data classification
inferred← citeshttps://doi.org/10.1016/j.inffus.2023.02.017openalex-snapshot
PARIS: Partial instance and training set selection. A new scalable approach to multi-label classification
inferred← citeshttps://doi.org/10.1016/j.ins.2023.01.090openalex-snapshot
A stochastic approximation approach to fixed instance selection
inferred← citeshttps://doi.org/10.1016/j.ins.2024.121738openalex-snapshot
SpIS: A stochastic approximation approach to minimal subset instance selection
inferred← citeshttps://doi.org/10.1016/j.knosys.2013.07.011openalex-snapshot
A study on the application of instance selection techniques in genetic fuzzy rule-based classification systems: Accuracy-complexity trade-off
inferred← citeshttps://doi.org/10.1016/j.neunet.2019.06.013openalex-snapshot
Improving the combination of results in the ensembles of prototype selectors
inferred← citeshttps://doi.org/10.1109/icdmw53433.2021.00108openalex-snapshot
Instance selection for multi-label learning based on a scalable evolutionary algorithm
inferred← citeshttps://doi.org/10.1109/tsusc.2025.3544430openalex-snapshot
Training Green AI Models Using Elite Samples
inferred← citeshttps://doi.org/10.1162/evco_a_00102openalex-snapshot
A Scalable Memetic Algorithm for Simultaneous Instance and Feature Selection
inferred← citeshttps://doi.org/10.22075/ijnaa.2021.4878openalex-snapshot
Outlier detection in test samples and supervised training set selection
inferred← citeshttps://doi.org/10.31274/etd-180810-2809openalex-snapshot
Instance selection for model-based classifiers
inferred← citeshttps://doi.org/10.31274/etd-180810-3375openalex-snapshot
A balanced approach to the multi-class imbalance problem
inferred← citeshttps://doi.org/10.3233/aic-160710openalex-snapshot
A survey on pre-processing techniques: Relevant issues in the context of environmental data mining
inferred← citeshttps://doi.org/10.3390/app12084024openalex-snapshot
On Training Data Selection in Condition Monitoring Applications—Case Azimuth Thrusters
inferred← citeshttps://doi.org/10.38016/jista.1033354openalex-snapshot
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