Early classification on time series

Xing, Zhengzheng; Pei, Jian; Yu, Philip S.

doi:10.1007/s10115-011-0400-x

Cited by 119 publications

(115 citation statements)

References 20 publications

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“…Following the work of Xing et al [37], we employed a 1-nearest neighbor (1NN) approach to automatically segregate simulations ending in ulcers from those that displayed resolving inflammation. The training set consisted of data from 100 simulations, labeled according to which endpoint was reached (resolved or ulcerated).…”

Section: -Nearest Neighbor Analysis Of Simulation Resultsmentioning

confidence: 99%

“…After ulceration, simulations with shorter pressure cycle lengths have more tissue damage initially. For comparison, the lower plot shows in vivo results [37] demonstrating that increasing the amount of ischemia increases damage (1h v 2h, 5 cycles) but for a given amount of ischemia, increasing the number of reperfusion events also increases damage (2h, 5 cycles v 1h, 10 cycles). …”

Section: Discussionmentioning

confidence: 99%

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A computational, tissue-realistic model of pressure ulcer formation in individuals with spinal cord injury

Ziraldo

Solovyev

Allegretti

et al. 2013

Journal of Critical Care

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People with spinal cord injury (SCI) are predisposed to pressure ulcers (PU). PU remain a significant burden in cost of care and quality of life despite improved mechanistic understanding and advanced interventions. An agent-based model (ABM) of ischemia/reperfusion-induced inflammation and PU (the PUABM) was created, calibrated to serial images of post-SCI PU, and used to investigate potential treatments in silico. Tissue-level features of the PUABM recapitulated visual patterns of ulcer formation in individuals with SCI. These morphological features, along with simulated cell counts and mediator concentrations, suggested that the influence of inflammatory dynamics caused simulations to be committed to "better" vs. "worse" outcomes by 4 days of simulated time and prior to ulcer formation. Sensitivity analysis of model parameters suggested that increasing oxygen availability would reduce PU incidence. Using the PUABM, in silico trials of anti-inflammatory treatments such as corticosteroids and a neutralizing antibody targeted at Damage-Associated Molecular Pattern molecules (DAMPs) suggested that, at best, early application at a sufficiently high dose could attenuate local inflammation and reduce pressure-associated tissue damage, but could not reduce PU incidence. The PUABM thus shows promise as an adjunct for mechanistic understanding, diagnosis, and design of therapies in the setting of PU. Author SummaryA virtual pressure ulcer was created as a platform to test therapies and determine the mechanisms most correlated with unfavorable outcomes. A layer of tissue fed with oxygen and diffusible molecules via blood vessels could develop an ulcer if pressure was applied, by simulating constriction of blood vessels in a circular region. Simulated ulcers were visually similar to digital photographs of ulcers in individuals with spinal cord injury in their irregular shapes, jagged edges, and overall progression in time. Statistical analyses of simulation outputs revealed that inflammation was an important determinant of ulcer severity and overall tissue damage. However, simulated clinical trials revealed that blocking the negative effects of inflammation could not prevent ulceration, and in order to be beneficial at all for this specific type of ulcer, anti-inflammatory treatments must be applied during the earliest stages of ulcer formation-before many clinical signs of ulceration appear.

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Section: -Nearest Neighbor Analysis Of Simulation Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A computational, tissue-realistic model of pressure ulcer formation in individuals with spinal cord injury

Ziraldo

Solovyev

Allegretti

et al. 2013

Journal of Critical Care

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“…A wide range of methods tackle the early classification problem without explicitly accounting for the cost of delaying the decision [1,4,5,6,7,10,13,14]. These methods di↵er in (i) the design of the early classifiers and (ii) the estimation of the optimal time to make a decision.…”

Section: Related Workmentioning

confidence: 99%

“…For di cult classification problems, they might tend to delay the decision even if future observations are unlikely to help. To bypass this limitation, Xing et al [13] present a method that relies on neighborhood properties. For a given training time series x i 2 T , optimal prediction time ⌧ (x i ) is set to the smallest ⌧ i such that the set of reverse nearest neighbors for x i is stable for all t ⌧ i .…”

Section: ⌧ (X)mentioning

confidence: 99%

“…Hence, two di↵erent costs clearly appear from this point of view : the cost of delaying the decision and the cost of misclassification. Most of the approaches for early classification in the literature focus on the design of algorithms that make a reliable decision from incomplete time series, without explicitly accounting for the cost of delaying the decision [1,4,5,6,7,10,13,14]. In these works, su cient guarantee of accuracy triggers the decision.…”

Section: Introductionmentioning

confidence: 99%

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Cost-Aware Early Classification of Time Series

Tavenard

Malinowski

2016

Machine Learning and Knowledge Discovery in Databases

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Abstract. In time series classification, two antagonist notions are at stake. On the one hand, in most cases, the sooner the time series is classified, the more rewarding. On the other hand, an early classification is more likely to be erroneous. Most of the early classification methods have been designed to take a decision as soon as su cient level of reliability is reached. However, in many applications, delaying the decision with no guarantee that the reliability threshold will be met in the future can be costly. Recently, a framework dedicated to optimizing a trade-o↵ between classification accuracy and the cost of delaying the decision was proposed, together with an algorithm that decides online the optimal time instant to classify an incoming time series. On top of this framework, we build in this paper two di↵erent early classification algorithms that optimize a trade-o↵ between decision accuracy and the cost of delaying the decision. These algorithms are non-myopic in the sense that, even when classification is delayed, they can provide an estimate of when the optimal classification time is likely to occur. Our experiments on real datasets demonstrate that the proposed approaches are more robust than existing methods.

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A novel approach for early malware detection

Sharma

Singh

2020

Trans Emerging Tel Tech

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Early classification of time series is valuable in many real‐world applications such as early disease prediction, early disaster prediction, and patient monitoring where data are generated over time. The main objective of early classification is to provide a reliable class prediction earliest in time. In general, whenever the early prediction time improves, the prediction accuracy decreases. Thus, the trade‐off between earliness and accuracy needs to be addressed. In this article, we proposed an optimization‐based early classification model for time series data using early stopping rules (ESRs) and a series of probabilistic classifiers. ESRs are developed through particle swarm optimization by minimizing the well‐defined cost function that considers the missclassification cost and delaying decision cost simultaneously. The experimental results on 30 standard datasets demonstrate good performance for early classification in comparison to state of the art methods. Also, the proposed model is tested for early malware detection on a real dataset and shows decent performance by balancing the accuracy and earliness.

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Early classification on time series

Cited by 119 publications

References 20 publications

A computational, tissue-realistic model of pressure ulcer formation in individuals with spinal cord injury

A computational, tissue-realistic model of pressure ulcer formation in individuals with spinal cord injury

Cost-Aware Early Classification of Time Series

A novel approach for early malware detection

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