Detalhes
Nome
Bruno Miguel VelosoCargo
Investigador SéniorDesde
01 março 2013
Nacionalidade
PortugalCentro
Laboratório de Inteligência Artificial e Apoio à DecisãoContactos
+351220402963
bruno.m.veloso@inesctec.pt
2024
Autores
Mastelini, SM; Veloso, B; Halford, M; de Carvalho, ACPDF; Gama, J;
Publicação
INFORMATION FUSION
Abstract
Nearest neighbor search (NNS) is one of the main concerns in data stream applications since similarity queries can be used in multiple scenarios. Online NNS is usually performed on a sliding window by lazily scanning every element currently stored in the window. This paper proposes Sliding Window-based Incremental Nearest Neighbors (SWINN), a graph-based online search index algorithm for speeding up NNS in potentially never-ending and dynamic data stream tasks. Our proposal broadens the application of online NNS-based solutions, as even moderately large data buffers become impractical to handle when a naive NNS strategy is selected. SWINN enables efficient handling of large data buffers by using an incremental strategy to build and update a search graph supporting any distance metric. Vertices can be added and removed from the search graph. To keep the graph reliable for search queries, lightweight graph maintenance routines are run. According to experimental results, SWINN is significantly faster than performing a naive complete scan of the data buffer while keeping competitive search recall values. We also apply SWINN to online classification and regression tasks and show that our proposal is effective against popular online machine learning algorithms.
2024
Autores
Davari, N; Veloso, B; Ribeiro, RP; da Gama, JMP;
Publicação
Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, SAC 2024, Avila, Spain, April 8-12, 2024
Abstract
2024
Autores
Alcoforado, A; Okamura, LH; Fama, IC; Dias Bueno, BF; Lavado, AM; Ferraz, TP; Veloso, B; Reali Costa, AH;
Publicação
Proceedings of the 16th International Conference on Computational Processing of Portuguese, PROPOR 2024, Santiago de Compostela, Galicia/Spain, 12-15 March, 2024
Abstract
2024
Autores
Molina, M; Ribeiro, RP; Veloso, B; Gama, J;
Publicação
Advances in Intelligent Data Analysis XXII - 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, April 24-26, 2024, Proceedings, Part I
Abstract
[No abstract available]
2023
Autores
Ribeiro, RP; Mastelini, SM; Davari, N; Aminian, E; Veloso, B; Gama, J;
Publicação
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II
Abstract
Predictive Maintenance applications are increasingly complex, with interactions between many components. Black-box models are popular approaches due to their predictive accuracy and are based on deep-learning techniques. This paper presents an architecture that uses an online rule learning algorithm to explain when the black-box model predicts rare events. The system can present global explanations that model the black-box model and local explanations that describe why the black-box model predicts a failure. We evaluate the proposed system using four real-world public transport data sets, presenting illustrative examples of explanations.
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