Salvador García
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Generalised Entropies for Decision Trees in Classification Under Monotonicity Constraints Open
Several decision‐making approaches involve ordinal labelling between feature values and decision outcomes. These issues refer to ordinal classification under monotonicity constraints. Recently, some machine learning approaches have been de…
Fractional Correspondence Framework in Detection Transformer Open
The Detection Transformer (DETR), by incorporating the Hungarian algorithm, has significantly simplified the matching process in object detection tasks. This algorithm facilitates optimal one-to-one matching of predicted bounding boxes to …
Changes in Analytes Related to Immunity in the Saliva of Pigs After Vaccination Against Lawsonia intracellularis Open
Lawsonia intracellularis is a Gram-negative, intracellular bacterium that can infect several animal species. In pigs, the bacteria cause porcine proliferative enteropathy, or ileitis. The wide spread of the pathogen produces a large impact…
Balancing Forecast Accuracy and Switching Costs in Online Optimization of Energy Management Systems Open
This study investigates the integration of forecasting and optimization in energy management systems, with a focus on the role of switching costs -- penalties incurred from frequent operational adjustments. We develop a theoretical and emp…
Smart Data Driven Decision Trees Ensemble Methodology for Imbalanced Big Data Open
Differences in data size per class, also known as imbalanced data distribution, have become a common problem affecting data quality. Big Data scenarios pose a new challenge to traditional imbalanced classification algorithms, since they ar…
Application of spatial uncertainty predictor in CNN-BiLSTM model using coronary artery disease ECG signals Open
This study aims to address the need for reliable diagnosis of coronary artery disease (CAD) using \nartificial intelligence (AI) models. Despite the progress made in mitigating opacity with \nexplainable AI (XAI) and uncertainty quantifica…
Metric learning for monotonic classification: turning the space up to the limits of monotonicity Open
This paper presents, for the first time, a distance metric learning algorithm for monotonic classification. Monotonic datasets arise in many real-world applications, where there exist order relations in the input and output variables, and …
View article: Multisource Semisupervised Adversarial Domain Generalization Network for Cross-Scene Sea-Land Clutter Classification
Multisource Semisupervised Adversarial Domain Generalization Network for Cross-Scene Sea-Land Clutter Classification Open
Deep learning (DL)-based sea\textendash land clutter classification for sky-wave over-the-horizon-radar (OTHR) has become a novel research topic. In engineering applications, real-time predictions of sea\textendash land clutter with existi…
View article: Hybrid Gromov–Wasserstein Embedding for Capsule Learning
Hybrid Gromov–Wasserstein Embedding for Capsule Learning Open
Capsule networks (CapsNets) aim to parse images into a hierarchy of objects, parts, and their relationships using a two-step process involving part-whole transformation and hierarchical component routing. However, this hierarchical relatio…
Improved binary differential evolution with dimensionality reduction mechanism and binary stochastic search for feature selection Open
Computer systems store massive amounts of data with numerous features, leading to the need to extract the most important features for better classification in a wide variety of applications. Poor performance of various machine learning alg…
Protecting Sensitive Tabular Data in Hybrid Clouds Open
Regulated industries, such as Healthcare and Finance, are starting to move parts of their data and workloads to the public cloud. However, they are still reluctant to trust the public cloud with their most sensitive records, and hence leav…
Multi-modality approaches for medical support systems: A systematic review of the last decade Open
Healthcare traditionally relies on single-modality approaches, which limit the information available for medical decisions. However, advancements in technology and the availability of diverse data sources have made it feasible to integrate…
On Forecast Stability Open
Forecasts are typically not produced in a vacuum but in a business context, where forecasts are generated on a regular basis and interact with each other. For decisions, it may be important that forecasts do not change arbitrarily, and are…
View article: Speech emotion recognition via multiple fusion under spatial–temporal parallel network
Speech emotion recognition via multiple fusion under spatial–temporal parallel network Open
The authors are grateful to the anonymous reviewers and the editor for their valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China (No. 61702066), the Chongqing Research Program of B…
Semi-Supervised Constrained Clustering: An In-Depth Overview, Ranked Taxonomy and Future Research Directions Open
Clustering is a well-known unsupervised machine learning approach capable of automatically grouping discrete sets of instances with similar characteristics. Constrained clustering is a semi-supervised extension to this process that can be …
Semi-supervised Clustering with Two Types of Background Knowledge: Fusing Pairwise Constraints and Monotonicity Constraints Open
This study addresses the problem of performing clustering in the presence of two types of background knowledge: pairwise constraints and monotonicity constraints. To achieve this, the formal framework to perform clustering under monotonici…
View article: Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization
Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization Open
Support vector machines (SVMs) are popular learning algorithms to deal with binary classification problems. They traditionally assume equal misclassification costs for each class; however, real-world problems may have an uneven class distr…
DSAA 2021 Open
The IEEE International Conference on Data Science and Advanced Analytics (DSAA) features its strong interdisciplinary synergy between statistics (sponsored by ASA), computing, and information/intelligence sciences (by IEEE and ACM), and cr…
An Indexing Algorithm Based on Clustering of Minutia Cylinder Codes for Fast Latent Fingerprint Identification Open
This work was supported in part by National Council of Science and Technology of Mexico (CONACyT), Mexico, through the Scholarship under Grant 492968.