24,735 research outputs found
Drift Detection using Uncertainty Distribution Divergence
Data generated from naturally occurring processes tends to be non-stationary. For example, seasonal and gradual changes in climate data and sudden changes in financial data. In machine learning the degradation in classifier performance due to such changes in the data is known as concept drift and there are many approaches to detecting and handling it.
Most approaches to detecting concept drift, however, make the assumption that true classes for test examples will be available at no cost shortly after classification and base the detection of concept drift on measures relying on these labels. The high labelling cost in many domains provides a strong motivation to reduce the number of labelled instances required to detect and handle concept drift.
Triggered detection approaches that do not require labelled instances to detect concept drift show great promise for achieving this. In this paper we present Confidence Distribution Batch Detection (CDBD), an approach that provides a signal correlated to changes in concept without using labelled data. This signal combined with a trigger and a rebuild policy can maintain classifier accuracy which, in most cases, matches the accuracy achieved using classification error based detection techniques but using only a limited amount of labelled data
Drift Detection Using Uncertainty Distribution Divergence
Concept drift is believed to be prevalent inmost data gathered from naturally occurring processes andthus warrants research by the machine learning community.There are a myriad of approaches to concept drift handlingwhich have been shown to handle concept drift with varyingdegrees of success.
However, most approaches make the keyassumption that the labelled data will be available at nolabelling cost shortly after classification, an assumption whichis often violated. The high labelling cost in many domainsprovides a strong motivation to reduce the number of labelledinstances required to handle concept drift. Explicit detectionapproaches that do not require labelled instances to detectconcept drift show great promise for achieving this.
Ourapproach Confidence Distribution Batch Detection (CDBD)provides a signal correlated to changes in concept without usinglabelled data. We also show how this signal combined with atrigger and a rebuild policy can maintain classifier accuracywhile using a limited amount of labelled data
Drift Detection Using Uncertainty Distribution Divergence
Concept drift is believed to be prevalent inmost data gathered from naturally occurring processes andthus warrants research by the machine learning community.There are a myriad of approaches to concept drift handlingwhich have been shown to handle concept drift with varyingdegrees of success.
However, most approaches make the keyassumption that the labelled data will be available at nolabelling cost shortly after classification, an assumption whichis often violated. The high labelling cost in many domainsprovides a strong motivation to reduce the number of labelledinstances required to handle concept drift. Explicit detectionapproaches that do not require labelled instances to detectconcept drift show great promise for achieving this.
Ourapproach Confidence Distribution Batch Detection (CDBD)provides a signal correlated to changes in concept without usinglabelled data. We also show how this signal combined with atrigger and a rebuild policy can maintain classifier accuracywhile using a limited amount of labelled data
Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels
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 time. Concept drift detection aims to detect such
drifts and adapt the model so as to mitigate any deterioration in the model's
predictive performance. Unfortunately, most existing concept drift detection
methods rely on a strong and over-optimistic condition that the true labels are
available immediately for all already classified instances. In this paper, a
novel Hierarchical Hypothesis Testing framework with Request-and-Reverify
strategy is developed to detect concept drifts by requesting labels only when
necessary. Two methods, namely Hierarchical Hypothesis Testing with
Classification Uncertainty (HHT-CU) and Hierarchical Hypothesis Testing with
Attribute-wise "Goodness-of-fit" (HHT-AG), are proposed respectively under the
novel framework. In experiments with benchmark datasets, our methods
demonstrate overwhelming advantages over state-of-the-art unsupervised drift
detectors. More importantly, our methods even outperform DDM (the widely used
supervised drift detector) when we use significantly fewer labels.Comment: Published as a conference paper at IJCAI 201
ILC Beam Energy Measurement by means of Laser Compton Backscattering
A novel, non-invasive method of measuring the beam energy at the
International Linear Collider is proposed. Laser light collides head-on with
beam particles and either the energy of the Compton scattered electrons near
the kinematic end-point is measured or the positions of the Compton
backscattered -rays, the edge electrons and the unscattered beam
particles are recorded. A compact layout for the Compton spectrometer is
suggested. It consists of a bending magnet and position sensitive detectors
operating in a large radiation environment. Several options for high spatial
resolution detectors are discussed. Simulation studies support the use of an
infrared or green laser and quartz fiber detectors to monitor the backscattered
photons and edge electrons. Employing a cavity monitor, the beam particle
position downstream of the magnet can be recorded with submicrometer precision.
Such a scheme provides a feasible and promising method to access the incident
beam energy with precisions of or better on a bunch-to-bunch basis
while the electron and positron beams are in collision.Comment: 47 pages, 26 figures, version as accepted by Nucl. Instr. Meth. A
after improvement
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