Multi-label punitive kNN with self-adjusting memory for drifting data streams

M Roseberry, B Krawczyk, A Cano - ACM Transactions on Knowledge …, 2019 - dl.acm.org
In multi-label learning, data may simultaneously belong to more than one class. When multi-
label data arrives as a stream, the challenges associated with multi-label learning are joined …

Amanda: Semi-supervised density-based adaptive model for non-stationary data with extreme verification latency

RS Ferreira, G Zimbrão, LGM Alvim - Information Sciences, 2019 - Elsevier
Abstract Concept drift refers to an alteration in the relations between input and output data in
the distribution over time. Thus, a gradual concept drift alludes to a smooth and gradual …

Sparse filtering based domain adaptation for mechanical fault diagnosis

Z Zhang, H Chen, S Li, Z An - Neurocomputing, 2020 - Elsevier
Recently, machine learning has achieved considerable success in the field of mechanical
fault diagnosis. Nevertheless, in many real-world applications, the original vibration data …

Sena: Similarity-based error-checking of neural activations

RS Ferreira, J Guerin, J Guiochet, H Waeselynck - ECAI 2023, 2023 - ebooks.iospress.nl
In this work, we propose SENA, a run-time monitor focused on detecting unreliable
predictions from machine learning (ML) classifiers. The main idea is that instead of trying to …

MPC with machine learning applied to resource allocation problem using lambda architecture

MP Dal Pont, RS Ferreira, WW Teixeira, DM Lima… - IFAC-PapersOnLine, 2019 - Elsevier
The resource allocation problem is the process of allocating limited resources for a vast
amount of tasks. Within this problem there are several important variants such as the …

Adaptive Multi-label Classification on Drifting Data Streams

M Roseberry - 2024 - scholarscompass.vcu.edu
Drifting data streams and multi-label data are both challenging problems. When multi-label
data arrives as a stream, the challenges of both problems must be addressed along with …

Runtime safety monitoring of ML-based perception functions in autonomous systems

RS Ferreira - 2023 - theses.hal.science
High-accurate machine learning (ML) image classifiers cannot guarantee that they will not
fail at operation. Thus, their deployment in safety-critical applications such as autonomous …

Contrôle de sécurité en cours d'exécution des fonctions de perception basées sur le ML dans les systèmes autonomes

RS Ferreira - 2023 - laas.hal.science
Les classificateurs d'images à apprentissage machine (ML) très précis ne peuvent pas
garantir qu'ils ne tomberont pas en panne en cours de fonctionnement. Par conséquent, leur …

[PDF][PDF] Real-time decision support system applied to distribution utility dispatches

M Dal Pont, W Teixeira - 2019 - cired-repository.org
A distribution utility has to deal with several customer calls regarding grid maintenance or
energy issues. Generally, when the proper channels receive a call from the customers, the …

[PDF][PDF] Handshake: classificador para streams de dados em ambientes não estacionários com latência extrema de verificação

LGM Alvim - 2018 - researchgate.net
Com a evolução dos sistemas computacionais e da tecnologia, os computadores
começaram a fazer alguns processos antes realizados por humanos. Classificação é um …