Results 191 to 200 of about 336,769 (240)

D2MR: A Framework for Recommending Drift Detection Method via Data Characteristic-based Rule Mapping in Machine Learning System

Okjoo Choi
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Iceberg detection and drift simulation

, 2015
Dierking, Wolfgang, Wesche, Christine, Marino, Armando   +2 more
openaire   +1 more source

Autonomous Hazardous Gas Detection Systems: A Systematic Review. [PDF]

Sensors (Basel)
Chew BK, Mahmud A, Singh H.
europepmc   +1 more source

Self-learning model fusion for network anomaly detection: A hybrid CNN-LSTM-transformer framework. [PDF]

PLoS One
Wang J, Huang N, Zhang H, Liu L, Fu Q, Cao K, Guo X, Jung H.   +7 more
europepmc   +1 more source

IVA-FL: An information-value-aware federated learning framework for Privacy-Preserving Financial data Risk Management. [PDF]

PLoS One
Wu J, Xu H, Zhang T, Xu R, Wu H, Huang Z.   +5 more
europepmc   +1 more source

Reinforcement learning driven adaptive active frequency drift for fast and reliable islanding detection. [PDF]

Sci Rep
Abo-Khalil AG, Sayed K, Mbungu NT, Elnady A, Ismail AAA, Siti MW.   +5 more
europepmc   +1 more source

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Detecting Covariate Drift with Explanations

2021
Detecting when there is a domain drift between training and inference data is important for any model evaluated on data collected in real time. Many current data drift detection methods only utilize input features to detect domain drift. While effective, these methods disregard the model’s evaluation of the data, which may be a significant source of ...
Steffen Castle, Robert Schwarzenberg, Mohsen Pourvali   +2 more
openaire   +1 more source

Learning with Drift Detection

2004
Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem of learning when the distribution that generate the examples changes over time. We present a method for detection of changes in the probability distribution of examples.
João Gama, Pedro Medas, Gladys Castillo, Pedro Rodrigues   +3 more
openaire   +1 more source

Interactive Process Drift Detection Framework

2021
This paper presents a novel tool for detecting drifts in process models. The tool targets the challenge of defining the better parameter configuration for detecting drifts by providing an interactive user interface. Using this interface, the user can quickly change the parameters and verify how the process evolved. The process evolution is presented in
Denise Maria Vecino Sato, Jean Paul Barddal, Edson Emilio Scalabrin   +2 more
openaire   +1 more source