Classifiers with a reject option for early time-series classification

Hatami, Nima; Chira, Camelia

doi:10.1109/ciel.2013.6613134

Cited by 44 publications

(43 citation statements)

References 22 publications

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“…For example, Evans et al [10] show that the monitoring of patients and early identification of physiologic deterioration can be used to raise alerts and prevent crises in hospitalized patients. Also, Ghalwash et al [11] mention early stock crisis identification; Bregón et al [12] apply early classification to classify different types of faults in a simulated industrial plant; Hatami and Chira [13] attempt to classify a set of different odors as early as possible by using odor signals obtained from a set of sensors with the aim of identifying chemical leaks. Finally, in Mori et al [14], an early classification approach is applied to detect and identify bird songs as early as possible, with the objective of saving memory and battery life of the recording devices.…”

Section: Introductionmentioning

confidence: 99%

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Early Classification of Time Series by Simultaneously Optimizing the Accuracy and Earliness

Mori

Mendiburu

Dasgupta

et al. 2018

IEEE Trans. Neural Netw. Learning Syst.

147

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The problem of early classification of time series appears naturally in contexts where the data, of temporal nature, is collected over time, and early class predictions are interesting or even required. The objective is to classify the incoming sequence as soon as possible, while maintaining suitable levels of accuracy in the predictions. Thus, we can say that the problem of early classification consists in optimizing two objectives simultaneously: accuracy and earliness. In this context, we present a method for early classification of time series based on combining a set of probabilistic classifiers together with a stopping rule. This stopping rule will act as a trigger and will tell us when to output a prediction or when to wait for more data, and it's main novelty lies in the fact that it is built by explicitly optimizing a cost function based on accuracy and earliness. We have selected a large set of benchmark datasets and 4 other state-ofthe-art early classification methods and we have evaluated and compared our framework obtaining superior results in terms of both earliness and accuracy.

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Section: Introductionmentioning

confidence: 99%

“…Different types of models and reliability conditions result in a wide variety of early classification methods such as those proposed in [13,16,17].…”

mentioning

confidence: 99%

Early Classification of Time Series by Simultaneously Optimizing the Accuracy and Earliness

Mori

Mendiburu

Dasgupta

et al. 2018

IEEE Trans. Neural Netw. Learning Syst.

147

View full text Add to dashboard Cite

show abstract

“…This method is able to predict the class of a test time series at any time, but the issue of estimating the optimal time to make the decision is not addressed. Hatami et al [5] propose an ensemble classifier with a reject option. A decision is made as soon as the agreement between all classifiers is above a certain threshold.…”

Section: ⌧ (X)mentioning

confidence: 99%

“…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%

“…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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Classifiers with a reject option for early time-series classification

Cited by 44 publications

References 22 publications

Early Classification of Time Series by Simultaneously Optimizing the Accuracy and Earliness

Early Classification of Time Series by Simultaneously Optimizing the Accuracy and Earliness

Cost-Aware Early Classification of Time Series

A novel approach for early malware detection

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