64 citations as recorded by crossref.

1. Training Artificial Neural Networks Using a Global Optimization Method That Utilizes Neural Networks I. Tsoulos & A. Tzallas 10.3390/ai4030027
2. Random Bits Forest: a Strong Classifier/Regressor for Big Data Y. Wang et al. 10.1038/srep30086
3. Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches D. Kafka & D. Wilke 10.1007/s10898-020-00921-z
4. The parametric sensitivity of CAM5's MJO J. Boyle et al. 10.1002/2014JD022507
5. Learning Functions and Classes Using Rules I. Tsoulos 10.3390/ai3030044
6. A simple and efficient architecture for trainable activation functions A. Apicella et al. 10.1016/j.neucom.2019.08.065
7. FT4cip: A new functional tree for classification in class imbalance problems L. Cañete-Sifuentes et al. 10.1016/j.knosys.2022.109294
8. Value proposition operationalization in peer-to-peer platforms using machine learning J. Ramos-Henríquez et al. 10.1016/j.tourman.2021.104288
9. Train Neural Networks with a Hybrid Method That Incorporates a Novel Simulated Annealing Procedure I. Tsoulos et al. 10.3390/appliedmath4030061
10. A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution I. Tsoulos & A. Tzallas 10.3390/app13148124
11. Random bits regression: a strong general predictor for big data Y. Wang et al. 10.1186/s41044-016-0010-4
12. Reduced order models for assessing CO2 impacts in shallow unconfined aquifers E. Keating et al. 10.1016/j.ijggc.2016.01.008
13. Bound the Parameters of Neural Networks Using Particle Swarm Optimization I. Tsoulos et al. 10.3390/computers12040082
14. Exploring High‐Dimensional Structure via Axis‐Aligned Decomposition of Linear Projections J. Thiagarajan et al. 10.1111/cgf.13416
15. A Sensitivity Analysis of Two Mesoscale Models: COAMPS and WRF C. Marzban et al. 10.1175/MWR-D-19-0271.1
16. Improving the Generalization Abilities of Constructed Neural Networks with the Addition of Local Optimization Techniques I. Tsoulos et al. 10.3390/a17100446
17. Tackling Climate Change with Machine Learning D. Rolnick et al. 10.1145/3485128
18. Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation I. Yamane et al. 10.1162/NECO_a_00844
19. QFC: A Parallel Software Tool for Feature Construction, Based on Grammatical Evolution I. Tsoulos 10.3390/a15080295
20. Constructing Features Using a Hybrid Genetic Algorithm I. Tsoulos 10.3390/signals3020012
21. Low regularity exponential-type integrators for the “good” Boussinesq equation H. Li & C. Su 10.1093/imanum/drac081
22. Deep learning with support vector data description S. Kim et al. 10.1016/j.neucom.2014.09.086
23. Application of the Fuzzy Approach for Evaluating and Selecting Relevant Objects, Features, and Their Ranges W. Paja 10.3390/e25081223
24. Ensemble simulations of inertial confinement fusion implosions R. Nora et al. 10.1002/sam.11344
25. The examination of the effect of the criterion for neural network’s learning on the effectiveness of the qualitative analysis of multidimensional data D. Jamróz 10.1007/s10115-020-01441-8
26. RN-SMOTE: Reduced Noise SMOTE based on DBSCAN for enhancing imbalanced data classification A. Arafa et al. 10.1016/j.jksuci.2022.06.005
27. An automated design process for short pulse laser driven opacity experiments M. Martin et al. 10.1016/j.hedp.2017.12.001
28. RFCL: A new under-sampling method of reducing the degree of imbalance and overlap R. Zhang et al. 10.1007/s10044-020-00929-x
29. A wireless sensor data-based coal mine gas monitoring algorithm with least squares support vector machines optimized by swarm intelligence techniques P. Chen et al. 10.1177/1550147718777440
30. Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant D. Lucas et al. 10.5194/acp-17-13521-2017
31. Adapting the Parameters of RBF Networks Using Grammatical Evolution I. Tsoulos et al. 10.3390/ai4040054
32. Locating the Parameters of RBF Networks Using a Hybrid Particle Swarm Optimization Method I. Tsoulos & V. Charilogis 10.3390/a16020071
33. Sensitivity Analysis of the Spatial Structure of Forecasts in Mesoscale Models: Noncontinuous Model Parameters C. Marzban et al. 10.1175/MWR-D-19-0321.1
34. A Two-Phase Evolutionary Method to Train RBF Networks I. Tsoulos et al. 10.3390/app12052439
35. Local Crossover: A New Genetic Operator for Grammatical Evolution I. Tsoulos et al. 10.3390/a17100461
36. Using Optimization Techniques in Grammatical Evolution I. Tsoulos et al. 10.3390/fi16050172
37. A Review and Experimental Comparison of Multivariate Decision Trees L. Canete-Sifuentes et al. 10.1109/ACCESS.2021.3102239
38. An extensive analysis of the interaction between missing data types, imputation methods, and supervised classifiers U. Garciarena & R. Santana 10.1016/j.eswa.2017.07.026
39. On the effect of model parameters on forecast objects C. Marzban et al. 10.5194/gmd-11-1577-2018
40. To what extent does ENSO rectify the tropical Pacific mean state? M. Xue & T. Li 10.1007/s00382-023-06750-6
41. What should we do when a model crashes? Recommendations for global sensitivity analysis of Earth and environmental systems models R. Sheikholeslami et al. 10.5194/gmd-12-4275-2019
42. A Rule-Based Method to Locate the Bounds of Neural Networks I. Tsoulos et al. 10.3390/knowledge2030024
43. Finding plausible and diverse variants of a climate model. Part II: development and validation of methodology A. Karmalkar et al. 10.1007/s00382-019-04617-3
44. Approximation trees: statistical reproducibility in model distillation Y. Zhou et al. 10.1007/s10618-022-00907-3
45. A genetic algorithm approach to optimising random forests applied to class engineered data E. Elyan & M. Gaber 10.1016/j.ins.2016.08.007
46. Calibrated simplex-mapping classification R. Heese et al. 10.1371/journal.pone.0279876
47. Applying Bounding Techniques on Grammatical Evolution I. Tsoulos et al. 10.3390/computers13050111
48. Application of all-relevant feature selection for the failure analysis of parameter-induced simulation crashes in climate models W. Paja et al. 10.5194/gmd-9-1065-2016
49. Extremely simple classifier based on fuzzy logic and gene expression programming J. Kluska & M. Madera 10.1016/j.ins.2021.05.041
50. Evaluation of scale-aware subgrid mesoscale eddy models in a global eddy-rich model B. Pearson et al. 10.1016/j.ocemod.2017.05.007
51. Uncertainty Analysis of Simulations of the Turn‐of‐the‐Century Drought in the Western United States G. Anderson et al. 10.1029/2017JD027824
52. Constructing the Bounds for Neural Network Training Using Grammatical Evolution I. Tsoulos et al. 10.3390/computers12110226
53. A novel Bayesian method for variable selection and estimation in binary quantile regression M. Dao et al. 10.1002/sam.11591
54. Multi-Layer Hybrid (MLH) balancing technique: A combined approach to remove data imbalance M. Islam & H. Mustafa 10.1016/j.datak.2022.102105
55. New Approach Based on Termite's Hill Building for Prediction of Successful Simulations in Climate Models M. Rahmani et al. 10.4018/IJSIR.2017070103
56. Machine Learning Predictions of a Multiresolution Climate Model Ensemble G. Anderson & D. Lucas 10.1029/2018GL077049
57. Selective Neuron Re-Computation (SNRC) for Error-Tolerant Neural Networks S. Liu et al. 10.1109/TC.2021.3056992
58. The Construction of a Majority-Voting Ensemble Based on the Interrelation and Amount of Information of Features F. Aydın & Z. Aslan 10.1093/comjnl/bxz118
59. Semisupervised Tangent Space Discriminant Analysis Y. Zhou & S. Sun 10.1155/2015/706180
60. Result-Based Re-computation for Error-Tolerant Classification by a Support Vector Machine S. Liu et al. 10.1109/TAI.2020.3028321
61. The use of Lorentzian distance metric in classification problems Y. Kerimbekov et al. 10.1016/j.patrec.2016.09.006
62. Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification M. Loog 10.1109/TPAMI.2015.2452921
63. Voting Margin: A Scheme for Error-Tolerant k Nearest Neighbors Classifiers for Machine Learning S. Liu et al. 10.1109/TETC.2019.2963268
64. When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores J. Wang et al. 10.1007/s00521-017-3102-9