Listar por autor "Eiras-Franco, Carlos"
Mostrando ítems 1-12 de 12
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A novel framework for generic Spark workload characterization and similar pattern recognition using machine learning
Garralda-Barrio, Mariano; Eiras-Franco, Carlos; Bolón-Canedo, Verónica (Elsevier, 2024-07)[Abstract]: Comprehensive workload characterization plays a pivotal role in comprehending Spark applications, as it enables the analysis of diverse aspects and behaviors. This understanding is indispensable for devising ... -
A scalable decision-tree-based method to explain interactions in dyadic data
Eiras-Franco, Carlos; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo; Bahamonde, Antonio (Elsevier, 2019-12)[Abstract]: Gaining relevant insight from a dyadic dataset, which describes interactions between two entities, is an open problem that has sparked the interest of researchers and industry data scientists alike. However, ... -
Case Study of Anomaly Detection and Quality Control of Energy Efficiency and Hygrothermal Comfort in Buildings
Eiras-Franco, Carlos; Flores, Miguel; Bolón-Canedo, Verónica; Zaragoza, Sonia; Fernández-Casal, Rubén; Naya, Salvador; Tarrío-Saavedra, Javier (2019)[Abstract] The aim of this work is to propose different statistical and machine learning methodologies for identifying anomalies and control the quality of energy efficiency and hygrothermal comfort in buildings. ... -
Fast anomaly detection with locality-sensitive hashing and hyperparameter autotuning
Meira, Jorge; Eiras-Franco, Carlos; Bolón-Canedo, Verónica; Marreiros, Goreti; Alonso-Betanzos, Amparo (Elsevier, 2022-08)[Abstract]: This paper presents LSHAD, an anomaly detection (AD) method based on Locality Sensitive Hashing (LSH), capable of dealing with large-scale datasets. The resulting algorithm is highly parallelizable and its ... -
Fast Distributed kNN Graph Construction Using Auto-tuned Locality-sensitive Hashing
Eiras-Franco, Carlos; Martínez Rego, David; Kanthan, Leslie; Piñeiro, César; Bahamonde, Antonio; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo (Association for Computing Machinery, 2020)[Abstract]: The k-nearest-neighbors (kNN) graph is a popular and powerful data structure that is used in various areas of Data Science, but the high computational cost of obtaining it hinders its use on large datasets. ... -
Interpretable market segmentation on high dimension data
Eiras-Franco, Carlos; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo; Bahamonde, Antonio (M D P I AG, 2018-09-17)[Abstract] Obtaining relevant information from the vast amount of data generated by interactions in a market or, in general, from a dyadic dataset, is a broad problem of great interest both for industry and academia. Also, ... -
Large scale anomaly detection in mixed numerical and categorical input spaces
Eiras-Franco, Carlos; Martínez Rego, David; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo; Bahamonde, Antonio (Elsevier, 2019)[Abstract]: This work presents the ADMNC method, designed to tackle anomaly detection for large-scale problems with a mixture of categorical and numerical input variables. A flexible parametric probability measure is ... -
Multithreaded and Spark parallelization of feature selection filters
Eiras-Franco, Carlos; Bolón-Canedo, Verónica; Ramos Garea, Sabela; González-Domínguez, Jorge; Alonso-Betanzos, Amparo; Touriño, Juan (2016)[Abstract]: Vast amounts of data are generated every day, constituting a volume that is challenging to analyze. Techniques such as feature selection are advisable when tackling large datasets. Among the tools that provide ... -
New scalable machine learning methods: beyond classification and regression
Eiras-Franco, Carlos (2019)[Abstract] The recent surge in data available has spawned a new and promising age of machine learning. Success cases of machine learning are arriving at an increasing rate as some algorithms are able to leverage immense ... -
Regression Tree Based Explanation for Anomaly Detection Algorithm
López-Riobóo Botana, Iñigo Luis; Eiras-Franco, Carlos; Alonso-Betanzos, Amparo (MDPI AG, 2020-08-18)[Abstract] This work presents EADMNC (Explainable Anomaly Detection on Mixed Numerical and Categorical spaces), a novel approach to address explanation using an anomaly detection algorithm, ADMNC, which provides accurate ... -
Scalable Feature Selection Using ReliefF Aided by Locality-Sensitive Hashing
Eiras-Franco, Carlos; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo; Bahamonde, Antonio (Wiley, 2021)[Abstract] Feature selection algorithms, such as ReliefF, are very important for processing high-dimensionality data sets. However, widespread use of popular and effective such algorithms is limited by their computational ... -
Sustainable personalisation and explainability in Dyadic Data Systems
Paz Ruza, Jorge; Eiras-Franco, Carlos; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo (2022)[Abstract]: Systems that rely on dyadic data, which relate entities of two types together, have become ubiquitously used in fields such as media services, tourism business, e-commerce, and others. However, these systems ...