Browsing Laboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA) by Issue Date
Now showing items 21-40 of 91
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Feature Selection With Limited Bit Depth Mutual Information for Embedded Systems
(MDPI AG, 2018-09-17)[Abstract] Data is growing at an unprecedented pace. With the variety, speed and volume of data flowing through networks and databases, newer approaches based on machine learning are required. But what is really big in Big ... -
Case Study of Anomaly Detection and Quality Control of Energy Efficiency and Hygrothermal Comfort in Buildings
(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. ... -
Insights into distributed feature ranking
(Elsevier, 2019)[Abstract]: In an era in which the volume and complexity of datasets is continuously growing, feature selection techniques have become indispensable to extract useful information from huge amounts of data. However, existing ... -
Ensembles for feature selection: A review and future trends
(Elsevier, 2019)[Abstract]: Ensemble learning is a prolific field in Machine Learning since it is based on the assumption that combining the output of multiple models is better than using a single model, and it usually provides good ... -
Large scale anomaly detection in mixed numerical and categorical input spaces
(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 ... -
On developing an automatic threshold applied to feature selection ensembles
(Elsevier, 2019-01)[Abstract]: Feature selection ensemble methods are a recent approach aiming at adding diversity in sets of selected features, improving performance and obtaining more robust and stable results. However, using an ensemble ... -
A Convolutional Network for Sleep Stages Classification
(2019-02)[Abstract]: Sleep stages classification is a crucial task in the context of sleep studies. It involves the simultaneous analysis of multiple signals recorded during sleep. However, it is complex and tedious, and even the ... -
Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry
(Elsevier Ltd, 2019-06)[Abstract]: Proactive Maintenance practices are becoming more standard in industrial environments, with a direct and profound impact on the competitivity within the sector. These practices demand the continuous monitorization ... -
Anomaly Detection in IoT: Methods, Techniques and Tools
(MDPI AG, 2019-07-22)[Abstract] Nowadays, the Internet of things (IoT) network, as system of interrelated computing devices with the ability to transfer data over a network, is present in many scenarios of everyday life. Understanding how ... -
A Machine Learning Solution for Distributed Environments and Edge Computing
(MDPI AG, 2019-08-09)[Abstract] In a society in which information is a cornerstone the exploding of data is crucial. Thinking of the Internet of Things, we need systems able to learn from massive data and, at the same time, being inexpensive ... -
Distributed classification based on distances between probability distributions in feature space
(Elsevier, 2019-09)[Abstract]: We consider a distributed framework where training and test samples drawn from the same distribution are available, with the training instances spread across disjoint nodes. In this setting, a novel learning ... -
Distributed correlation-based feature selection in spark
(Elsevier, 2019-09)[Abstract]: Feature selection (FS) is a key preprocessing step in data mining. CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We ... -
A scalable saliency-based feature selection method with instance-level information
(Elsevier, 2019-11)[Abstract]: Classic feature selection techniques remove irrelevant or redundant features to achieve a subset of relevant features in compact models that are easier to interpret and so improve knowledge extraction. Most ... -
Wavefront Marching Methods: A Unified Algorithm to Solve Eikonal and Static Hamilton-Jacobi Equations
(IEEE, 2019-12)[Abstract]: This paper presents a unified propagation method for dealing with both the classic Eikonal equation, where the motion direction does not affect the propagation, and the more general static Hamilton-Jacobi ... -
A scalable decision-tree-based method to explain interactions in dyadic data
(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, ... -
Community detection and social network analysis based on the Italian wars of the 15th century
(Elsevier, 2020)[Abstract]: In this contribution we study social network modelling by using human interaction as a basis. To do so, we propose a new set of functions, affinities, designed to capture the nature of the local interactions ... -
Fast Distributed kNN Graph Construction Using Auto-tuned Locality-sensitive Hashing
(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. ... -
Usability Heuristics for Domain-Specific Languages (DSLs)
(ACM, 2020-03-30)[Abstract] The usability of Domain-Specific Languages (DSLs) has been attracting considerable interest from researchers lately. In particular, our literature review found many usability studies that make use of subjective ... -
Feature selection with limited bit depth mutual information for portable embedded systems
(Elsevier, 2020-06)[Abstract]: Since wearable computing systems have grown in importance in the last years, there is an increased interest in implementing machine learning algorithms with reduced precision parameters/computations. Not only ... -
Regression Tree Based Explanation for Anomaly Detection Algorithm
(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 ...
