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

Ferreira, Raul Sena; Zimbrão, Geraldo; Alvim, Leandro G.M.

doi:10.1016/j.ins.2019.03.025

Cited by 28 publications

(10 citation statements)

References 21 publications

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“…The accuracy of the proposed NEVE approaches was also compared with DWM [26], Learn ++, NSE [9], RCD [16], EFPT [55] and AMANDA [56] models. We used 3 different drift detectors for the RCD algorithm: DDM [14], EDDM [5] and ECDD [42].…”

Section: Resultsmentioning

confidence: 99%

“…A more successful and widely used approach though is to use a group of different classifiers (ensemble) to cope with changes in the environment. Several different ensemble models have been proposed in the literature, including recent approaches like [56][57][58], and may or may not weigh each of its members. Most models using weighted classifier ensembles determine the weights for each classifier using a set of heuristics related to classifier performance in the most recent data received [22].…”

Section: Introductionmentioning

confidence: 99%

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Neuroevolutionary learning in nonstationary environments

Escovedo

Koshiyama

Cruz³

et al. 2020

Appl Intell

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This work presents a new neuro-evolutionary model, called NEVE (Neuroevolutionary Ensemble), based on an ensemble of Multi-Layer Perceptron (MLP) neural networks for learning in nonstationary environments. NEVE makes use of quantuminspired evolutionary models to automatically configure the ensemble members and combine their output. The quantuminspired evolutionary models identify the most appropriate topology for each MLP network, select the most relevant input variables, determine the neural network weights and calculate the voting weight of each ensemble member. Four different approaches of NEVE are developed, varying the mechanism for detecting and treating concepts drifts, including proactive drift detection approaches. The proposed models were evaluated in real and artificial datasets, comparing the results obtained with other consolidated models in the literature. The results show that the accuracy of NEVE is higher in most cases and the best configurations are obtained using some mechanism for drift detection. These results reinforce that the neuroevolutionary ensemble approach is a robust choice for situations in which the datasets are subject to sudden changes in behaviour.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neuroevolutionary learning in nonstationary environments

Escovedo

Koshiyama

Cruz³

et al. 2020

Appl Intell

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“…However, expanding the prediction horizon to N p > 1 should present better dispatch strategies and needs more experimental tests. Finally, we highlight some directions for further research: 1)Density approaches aiming to reduce the bias of the learning model induced by the similar customer's calls contained in the stream [Ferreira et al 2018a]; 2)Since some characteristics of this real-time dataset are non-stationary, learning models for concept-drift can improve the results [Ferreira et al 2018b]; and 3)Development of better MPC objective function with other assumptions such as the total time that a customer does not have energy by month and the customer's location.…”

Section: Discussionmentioning

confidence: 99%

RDI: A Real-time Decision Support System Applied to Dispatch Decision Problem

Ferreira¹,

Pont²,

Silva³

et al. 2018

Anais Da v Escola Regional De Sistemas De Informação Do Rio De Janeiro

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“…• Distributional shifts: a condition that decreases the ML performance through time since the training dataset may differ from the real inputs. Such a situation is also known as a concept-drift [13]. Examples of distributional shift include a change in class attributes such as dimension, physical characteristics, contrast, brightness, and other pixel-related variations in the image.…”

Section: B Out-of-distribution Datamentioning

confidence: 99%

Benchmarking Safety Monitors for Image Classifiers with Machine Learning

Ferreira

Arlat

Guiochet

et al. 2021

2021 IEEE 26th Pacific Rim International Symposium on Dependable Computing (PRDC)

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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 vehicles is still an open issue. The use of fault tolerance mechanisms such as safety monitors is a promising direction to keep the system in a safe state despite errors of the ML classifier. As the prediction from the ML is the core information directly impacting safety, many works are focusing on monitoring the ML model itself. Checking the efficiency of such monitors in the context of safety-critical applications is thus a significant challenge. Therefore, this paper aims at establishing a baseline framework for benchmarking monitors for ML image classifiers. Furthermore, we propose a framework covering the entire pipeline, from data generation to evaluation. Our approach measures monitor performance with a broader set of metrics than usually proposed in the literature. Moreover, we benchmark three different monitor approaches in 79 benchmark datasets containing five categories of out-of-distribution data for image classifiers: class novelty, noise, anomalies, distributional shifts, and adversarial attacks. Our results indicate that these monitors are no more accurate than a random monitor. We also release the code of all experiments for reproducibility.

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

Cited by 28 publications

References 21 publications

Neuroevolutionary learning in nonstationary environments

Neuroevolutionary learning in nonstationary environments

RDI: A Real-time Decision Support System Applied to Dispatch Decision Problem

Benchmarking Safety Monitors for Image Classifiers with Machine Learning

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