Browsing by Keywords "Concept drift"
Now showing items 1-10 of 10
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BELS: a broad ensemble learning system for data stream classification
(Bilkent University, 2021-12)Data stream classification has become a major research topic due to the increase in temporal data. One of the biggest hurdles of data stream classification is the development of algorithms that deal with evolving data, ... -
Concept learning using one-class classifiers for implicit drift detection in evolving data streams
(Springer, 2021-06)Data stream mining has become an important research area over the past decade due to the increasing amount of data available today. Sources from various domains generate a near-limitless volume of data in temporal order. ... -
Evolving text stream classification with a novel neural ensemble architecture
(Bilkent University, 2022-01)We study on-the-fly classification of evolving text streams in which the relation between the input data target labels changes over time—i.e. “concept drift”. These variations decrease the model’s performance, as predictions ... -
Goowe : geometrically optimum and online-weighted ensemble classifier for evolving data streams
(Bilkent University, 2016-07)Designing adaptive classifiers for an evolving data stream is a challenging task due to its size and dynamically changing nature. Combining individual classifiers in an online setting, the ensemble approach, is one of the ... -
GOOWE: geometrically optimum and online-weighted ensemble classifier for evolving data streams
(Association for Computing Machinery, 2018-01-25)Designing adaptive classifiers for an evolving data stream is a challenging task due to the data size and its dynamically changing nature. Combining individual classifiers in an online setting, the ensemble approach, is a ... -
Implicit concept drift detection for multi-label data streams
(Bilkent University, 2022-01)Many real-world applications adopt multi-label data streams as the need for algo-rithms to deal with rapidly generated data increases. For such streams, changes in data distribution, also known as concept drift, cause the ... -
On-the-fly ensemble classifier pruning in evolving data streams
(Bilkent University, 2019-09)Ensemble pruning is the process of selecting a subset of component classifiers from an ensemble which performs at least as well as the original ensemble while reducing storage and computational costs. Ensemble pruning ... -
Unsupervised concept drift detection for multi-label data streams
(Springer, 2022-07-17)Many real-world applications adopt multi-label data streams as the need for algorithms to deal with rapidly changing data increases. Changes in data distribution, also known as concept drift, cause existing classification ... -
Unsupervised concept drift detection using sliding windows: two contributions
(Bilkent University, 2020-10)Data stream mining has become an important research area over the past decade due to the increasing amount of data available today. Sources from various domains generate limitless volume of data in temporal order. Such ... -
Unsupervised concept drift detection with a discriminative classifier
(Association for Computing Machinery, 2019)In data stream mining, one of the biggest challenges is to develop algorithms that deal with the changing data. As data evolve over time, static models become outdated. This phenomenon is called concept drift, and it is ...
