Results 31 to 40 of about 336,769 (240)

Multilayer Concept Drift Detection Method Based on Model Explainability

IEEE Access
Timely detection of concept drift plays a vital role in ensuring the stability and reliability of data-driven models. However, existing concept drift detection methods face challenges in achieving a proper balance between accuracy and timeliness while ...
Haolan Zhang, Xinyi Chen, Min Hu, Vijayan Sugumaran   +3 more
doaj   +1 more source

A compact high resolution ion mobility spectrometer for fast trace gas analysis [PDF]

, 2013
Drift tube ion mobility spectrometers (IMS) are widely used for fast trace gas detection in air, but portable compact systems are typically very limited in their resolving power.
Allers, Maria, Cochems, Philipp, Kirk, Ansgar T., Langejuergen, Jens, Zimmermann, Stefan   +4 more
core   +2 more sources

One or two things we know about concept drift—a survey on monitoring in evolving environments. Part B: locating and explaining concept drift

Frontiers in Artificial Intelligence
In an increasing number of industrial and technical processes, machine learning-based systems are being entrusted with supervision tasks. While they have been successfully utilized in many application areas, they frequently are not able to generalize to ...
Fabian Hinder, Valerie Vaquet, Barbara Hammer   +2 more
doaj   +1 more source

Anticipating Vehicle Drift in the Real-World: A Data-Driven Intelligence Layer for Future AVs

IEEE Access
Extreme driving scenarios, such as drifting, pose significant risks to driving safety. This article proposes a novel intelligence layer for vehicle drift detection, characterisation and anticipation, fostering the development of Advanced Driver ...
Ines Rito Lima, Yukie Nagai, Jose Santos-Victor   +2 more
doaj   +1 more source

Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels

, 2018
One important assumption underlying common classification models is the stationarity of the data. However, in real-world streaming applications, the data concept indicated by the joint distribution of feature and label is not stationary but drifting over
Principe, Jose C., Wang, Xiaoyang, Yu, Shujian   +2 more
core   +1 more source

Severity-Aware Drift Adaptation for Cost-Efficient Model Maintenance

AI
Objectives: This paper introduces an adaptive learning framework for handling concept drift in data by dynamically adjusting model updates based on the severity of detected drift.
Khrystyna Shakhovska, Petro Pukach
doaj   +1 more source

One or two things we know about concept drift—a survey on monitoring in evolving environments. Part A: detecting concept drift

Frontiers in Artificial Intelligence
The world surrounding us is subject to constant change. These changes, frequently described as concept drift, influence many industrial and technical processes.
Fabian Hinder, Valerie Vaquet, Barbara Hammer   +2 more
doaj   +1 more source

Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams

, 2017
The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting computer security violations.
Paquet, Eric, Pesaranghader, Ali, Viktor, Herna   +2 more
core   +1 more source

Astro2020 White Paper: A Direct Measure of Cosmic Acceleration [PDF]

, 2019
Nearly a century after the discovery that we live in an expanding Universe, and two decades after the discovery of accelerating cosmic expansion, there remains no direct detection of this acceleration via redshift drift - a change in the cosmological ...
Conklin, John, Darling, Jeremy, Dong, Chenxing, Eikenberry, Stephen, Fulda, Paul, Gonzalez, Anthony, Jeram, Sarik, Likamonsavad, Manunya, Mueller, Guido, Slepian, Zachary, Townsend, Amanda   +10 more
core   +1 more source

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

, 2019
Existing FNNs are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be automatically extracted from
Pedrycz, Witold, Pratama, Mahardhika, Webb, Geoffrey I.   +2 more
core   +1 more source