ListarLaboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA) por tema "Scalability"
Mostrando ítems 1-8 de 8
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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 ... -
Fast anomaly detection with locality-sensitive hashing and hyperparameter autotuning
(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
(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
(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
(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 the scalability of feature selection methods on high-dimensional data
(Springer, 2018)[Abstract]: Lately, derived from the explosion of high dimensionality, researchers in machine learning became interested not only in accuracy, but also in scalability. Although scalability of learning methods is a trending ... -
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 ... -
Scalable Feature Selection Using ReliefF Aided by Locality-Sensitive Hashing
(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 ...
