Monitoring Incremental Histogram Distribution for Change Detection in Data Streams

Sebastião, Raquel; Gama, João; Rodrigues, Pedro Pereira; Bernardes, João

doi:10.1007/978-3-642-12519-5_2

Cited by 18 publications

(13 citation statements)

References 22 publications

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“…More methods could be tested, for example, the Fixed Cumulative Windows Model (FCWM) (Sebastião, Gama, Rodrigues, & Bernardes, 2010), as well as other base learners.…”

Section: Resultsmentioning

confidence: 99%

A comparative study on concept drift detectors

Gonçalvès

Santos

Barros

et al. 2014

Expert Systems with Applications

147

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“…More methods could be tested, for example, the Fixed Cumulative Windows Model (FCWM) (Sebastião, Gama, Rodrigues, & Bernardes, 2010), as well as other base learners.…”

Section: Resultsmentioning

confidence: 99%

A comparative study on concept drift detectors

Gonçalvès

Santos

Barros

et al. 2014

Expert Systems with Applications

147

View full text Add to dashboard Cite

“…However, based on the Kullback-Leibler divergence they can be used on purely continuous and, as demonstrated in this work, even in mixed-types multi-variate distributions. Dasu et al (2009) and Sebastião et al (2010) did use the Kullback-Leibler divergence in their respective studies. However, the Kullback-Leibler neither satisfies the properties of a metric nor is bounded, as required in this study.…”

Section: Comparison With Related Workmentioning

confidence: 99%

Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality

Sáez

Rodrigues

Gama

et al. 2014

Data Min Knowl Disc

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Knowledge discovery on biomedical data can be based on on-line, data-stream analyses, or using retrospective, timestamped, off-line datasets. In both cases, changes in the processes that generate data or in their quality features through time may hinder either the knowledge discovery process or the generalization of past knowledge. These problems can be seen as a lack of data temporal stability. This work establishes the temporal stability as a data quality dimension and proposes new methods for its assessment based on a probabilistic framework. Concretely, methods are proposed for 1) monitoring changes, and 2) characterizing changes, trends and detecting temporal subgroups. First, a probabilistic change detection algorithm is proposed based on the Statistical Process Control of the posterior Beta distribution of the Jensen-Shannon distance, with a memoryless forgetting mechanism. This algorithm (PDF-SPC) classifies the degree of current change in three states: In-Control, Warning, and Out-of-Control. Second, a novel method is proposed to visualize and characterize the temporal changes of data based on the projection of a non-parametric information-geometric statistical manifold of time windows. This projection facilitates the exploration of temporal trends using the proposed IGT-plot and, by means of unsupervised learning methods, discovering conceptually-related temporal subgroups. Methods are evaluated using real and simulated data based on the National Hospital Discharge Survey (NHDS) dataset.

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“…Recently, several methods capable of dealing with change detection have been proposed [3,4,12,13,15,26]. Drifting concepts are often handled by time windows or weighted samples according to their "'age"' or utility.…”

Section: The Change Detection Problemmentioning

confidence: 99%

“…performance measures, data distribution, properties of the data, etc.) can be monitored over time [5,12,23,26].…”

Section: The Change Detection Problemmentioning

confidence: 99%

Real-time algorithm for changes detection in depth of anesthesia signals

et al. 2012

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This paper presents a real-time algorithm for changes detection in depth of anesthesia signals. A Page-Hinkley test (PHT) with a forgetting mechanism (PHT-FM) was developed. The samples are weighted according to their "age" so that more importance is given to recent samples. This enables the detection of the changes with less time delay than if no forgetting factor was used. The performance of the PHT-FM was evaluated in a twofold approach. First, the algorithm was run offline in DoA signals previously collected during general anesthesia, allowing the adjustment of the forgetting mechanism. Second, the PHT-FM was embedded in a real-time software and its performance was validated online in the surgery room. This was performed by asking the clinician to classify in real-time the changes as true positives, false positives or false negatives. The results show that 69% of the changes were classified as true positives, 26% as false positives, and 5% as false negatives. The true positives were also synchronized with changes in the hypnotic or analgesic rates made by the clinician. The contribution of this work has a high impact in the clinical practice since the PHT-FM alerts the clinician for changes in the anesthetic state of the patient, allowing a more prompt action. The results encourage the inclusion of the proposed PHT-FM in a real-time decision support system for routine use in the clinical practice.

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Monitoring Incremental Histogram Distribution for Change Detection in Data Streams

Cited by 18 publications

References 22 publications

A comparative study on concept drift detectors

A comparative study on concept drift detectors

Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality

Real-time algorithm for changes detection in depth of anesthesia signals

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