| Unimodal regularisation based on beta distribution for deep ordinal regression | 0 | 0.34 | 2022 |
| ReLU-Based Activations - Analysis and Experimental Study for Deep Learning. | 0 | 0.34 | 2021 |
| Studying the Effect of Different Lp Norms in the Context of Time Series Ordinal Classification. | 0 | 0.34 | 2021 |
| Multi-task learning for the prediction of wind power ramp events with deep neural networks. | 3 | 0.52 | 2020 |
| Ordinal Versus Nominal Time Series Classification. | 0 | 0.34 | 2020 |
| Optimising Convolutional Neural Networks using a Hybrid Statistically-driven Coral Reef Optimisation algorithm | 4 | 0.41 | 2020 |
| Statistically-driven Coral Reef metaheuristic for automatic hyperparameter setting and architecture design of Convolutional Neural Networks. | 1 | 0.34 | 2020 |
| Ordinal Multi-class Architecture for Predicting Wind Power Ramp Events Based on Reservoir Computing | 0 | 0.34 | 2020 |
| Multi-objective evolutionary optimization using the relationship between F1 and accuracy metrics in classification tasks | 0 | 0.34 | 2019 |
| A hybrid dynamic exploitation barebones particle swarm optimisation algorithm for time series segmentation | 1 | 0.34 | 2019 |
| On the use of evolutionary time series analysis for segmenting paleoclimate data. | 2 | 0.38 | 2019 |
| Efficient fog prediction with multi-objective evolutionary neural networks. | 2 | 0.35 | 2018 |
| Hybrid Weighted Barebones Exploiting Particle Swarm Optimization Algorithm for Time Series Representation. | 0 | 0.34 | 2018 |
| Time series clustering based on the characterisation of segment typologies. | 0 | 0.34 | 2018 |
| Time series forecasting by recurrent product unit neural networks. | 1 | 0.35 | 2018 |
| An Empirical Validation of a New Memetic CRO Algorithm for the Approximation of Time Series. | 0 | 0.34 | 2018 |
| Sensitivity versus accuracy in ensemble models of Artificial Neural Networks from Multi-objective Evolutionary Algorithms. | 3 | 0.37 | 2018 |
| Fine-to-Coarse Ranking in Ordinal and Imbalanced Domains: An Application to Liver Transplantation. | 1 | 0.35 | 2017 |
| Combining Reservoir Computing and Over-Sampling for Ordinal Wind Power Ramp Prediction. | 1 | 0.38 | 2017 |
| Identifying Market Behaviours Using European Stock Index Time Series by a Hybrid Segmentation Algorithm. | 1 | 0.40 | 2017 |
| Synthetic semi-supervised learning in imbalanced domains: Constructing a model for donor-recipient matching in liver transplantation. | 0 | 0.34 | 2017 |
| Fisher Score-Based Feature Selection For Ordinal Classification: A Social Survey On Subjective Well-Being | 0 | 0.34 | 2016 |
| A Study on Multi-Scale Kernel Optimisation via Centered Kernel-Target Alignment | 2 | 0.37 | 2016 |
| Oversampling the Minority Class in the Feature Space. | 8 | 0.44 | 2016 |
| Learning from Label Proportions via an Iterative Weighting Scheme and Discriminant Analysis | 0 | 0.34 | 2016 |
| Ordinal Evolutionary Artificial Neural Networks For Solving An Imbalanced Liver Transplantation Problem | 2 | 0.37 | 2016 |
| Time Series Representation By A Novel Hybrid Segmentation Algorithm | 2 | 0.37 | 2016 |
| Classification Of Melanoma Presence And Thickness Based On Computational Image Analysis | 3 | 0.37 | 2016 |
| Kernelising the Proportional Odds Model through kernel learning techniques | 2 | 0.36 | 2015 |
| Nonlinear Ordinal Logistic Regression Using Covariates Obtained by Radial Basis Function Neural Networks Models | 0 | 0.34 | 2015 |
| Classification of countries' progress toward a knowledge economy based on machine learning classification techniques. | 0 | 0.34 | 2015 |
| Applying a Hybrid Algorithm to the Segmentation of the Spanish Stock Market Index Time Series | 2 | 0.39 | 2015 |
| Energy Flux Range Classification by Using a Dynamic Window Autoregressive Model | 0 | 0.34 | 2015 |
| Significant wave height and energy flux range forecast with machine learning classifiers | 6 | 0.99 | 2015 |
| An Experimental Comparison for the Identification of Weeds in Sunflower Crops via Unmanned Aerial Vehicles and Object-Based Analysis | 1 | 0.41 | 2015 |
| Logistic evolutionary product-unit neural network classifier: the case of agrarian efficiency. | 0 | 0.34 | 2015 |
| Graph-Based Approaches for Over-Sampling in the Context of Ordinal Regression | 17 | 0.63 | 2015 |
| Evolutionary Product Unit Logistic Regression: The Case of Agrarian Efficiency | 0 | 0.34 | 2015 |
| Metrics to guide a multi-objective evolutionary algorithm for ordinal classification. | 23 | 0.79 | 2014 |
| Projection-based ensemble learning for ordinal regression. | 6 | 0.43 | 2014 |
| Time Series Segmentation and Statistical Characterisation of the Spanish Stock Market Ibex-35 Index | 0 | 0.34 | 2014 |
| An evolutionary neural system for incorporating expert knowledge into the UA-FLP. | 4 | 0.39 | 2014 |
| Ordinal regression neural networks based on concentric hyperspheres | 6 | 0.53 | 2014 |
| A guided data projection technique for classification of sovereign ratings: The case of European Union 27 | 3 | 0.36 | 2014 |
| Classification of EU countries' progress towards sustainable development based on ordinal regression techniques. | 4 | 0.40 | 2014 |
| Object-Based Image Classification of Summer Crops with Machine Learning Methods | 23 | 1.10 | 2014 |
| Time Series Segmentation of Paleoclimate Tipping Points by an Evolutionary Algorithm | 1 | 0.36 | 2014 |
| Learning Kernel Label Decompositions for Ordinal Classification Problems. | 0 | 0.34 | 2014 |
| Incorporating Privileged Information to Improve Manifold Ordinal Regression. | 0 | 0.34 | 2014 |
| Kernelizing the proportional odds model through the empirical kernel mapping | 1 | 0.35 | 2013 |
