LTSpAUC: Learning Time-series Shapelets for Optimizing Partial AUC

Yamaguchi, Akihiro; Maya, Shigeru; Maruchi, Kohei; Ueno, Ken

doi:10.1137/1.9781611976236.1

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…The first published version of this work is included in the proceedings of SIAM SDM 2020. 17 The present article is an invited journal extension of Ref. 17 as part of the ''best of SDM 2020'' articles.…”

Section: Contributions and Outlinementioning

confidence: 99%

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LTSpAUC: Learning Time-Series Shapelets for Partial AUC Maximization

et al. 2020

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Shapelets are discriminative segments used to classify time-series instances. Shapelet methods that jointly learn both classifiers and shapelets have been studied in recent years because such methods provide both interpretable results and superior accuracy. The partial area under the receiver operating characteristic curve (pAUC) for a low range of false-positive rates (FPR) is an important performance measure for practical cases in industries such as medicine, manufacturing, and maintenance. In this article, we propose a method that jointly learns both shapelets and a classifier for pAUC optimization in any FPR range, including the full AUC. In addition, we propose the following two extensions for shapelet methods: (1) reducing algorithmic complexity in time-series length to linear time and (2) explicitly determining the classes that shapelets tend to match. Comparing with state-of-theart learning-based shapelet methods, we demonstrated the superiority of pAUC on UCR time-series data sets and its effectiveness in industrial case studies from medicine, manufacturing, and maintenance.

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Section: Contributions and Outlinementioning

confidence: 99%

“…17). It is important to learn shapelets effective for classifying negative instances in a specific FPR range[a, b].…”

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confidence: 99%

LTSpAUC: Learning Time-Series Shapelets for Partial AUC Maximization

et al. 2020

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“…Later on [48], [50] provide refinement of [48] by formulating a tight convex upper bound of the original objective function. Recently, [26], [72] also extend this framework to timeseries prediction and semi-supervised learning. [29] proposes an online learning method for OPAUC optimization with sublinear regret and online to batch conversion bounds.…”

Section: Partial Auc Optimizationmentioning

confidence: 99%

Optimizing Two-Way Partial AUC With an End-to-End Framework

Yang

Bao

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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The Area Under the ROC Curve (AUC) is a crucial metric for machine learning, which evaluates the average performance over all possible True Positive Rates (TPRs) and False Positive Rates (FPRs). Based on the knowledge that a skillful classifier should simultaneously embrace a high TPR and a low FPR, we turn to study a more general variant called Two-way Partial AUC (TPAUC), where only the region with TPR ≥ α, FPR ≤ β is included in the area. Moreover, a recent work shows that the TPAUC is essentially inconsistent with the existing Partial AUC metrics where only the FPR range is restricted, opening a new problem to seek solutions to leverage high TPAUC. Motivated by this, we present the first trial in this paper to optimize this new metric. The critical challenge along this course lies in the difficulty of performing gradient-based optimization with end-to-end stochastic training, even with a proper choice of surrogate loss. To address this issue, we propose a generic framework to construct surrogate optimization problems, which supports efficient end-to-end training with deep learning. Moreover, our theoretical analyses show that: 1) the objective function of the surrogate problems will achieve an upper bound of the original problem under mild conditions, and 2) optimizing the surrogate problems leads to good generalization performance in terms of TPAUC with a high probability. Finally, empirical studies over several benchmark datasets speak to the efficacy of our framework.

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“…This is achieved by using DTW similarity instead of Euclidean distance, since it is better adapted to measure the similarity between the shapelets and the time-series. Another approach for learning shapelets is to optimise the partial AUC [29], in which shapelets are learnt in conjunction with a classifier.…”

Section: Learning Shapeletsmentioning

confidence: 99%

CDPS: Constrained DTW-Preserving Shapelets

Amouri

Lampert

Gançarski

et al. 2023

Lecture Notes in Computer Science

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The analysis of time series for clustering and classification is becoming ever more popular because of the increasingly ubiquitous nature of IoT, satellite constellations, and handheld and smart-wearable devices, etc. The presence of phase shift, differences in sample duration, and/or compression and dilation of a signal means that Euclidean distance is unsuitable in many cases. As such, several similarity measures specific to time-series have been proposed, Dynamic Time Warping (DTW) being the most popular. Nevertheless, DTW does not respect the axioms of a metric and therefore Learning DTW-Preserving Shapelets (LDPS) have been developed to regain these properties by using the concept of shapelet transform. LDPS learns an unsupervised representation that models DTW distances using Euclidean distance in shapelet space. This article proposes constrained DTW-preserving shapelets (CDPS), in which a limited amount of user knowledge is available in the form of must link and cannot link constraints, to guide the representation such that it better captures the user's interpretation of the data rather than the algorithm's bias. Subsequently, any unconstrained algorithm can be applied, e.g. K-means clustering, k-NN classification, etc, to obtain a result that fulfils the constraints (without explicit knowledge of them). Furthermore, this representation is generalisable to out-of-sample data, overcoming the limitations of standard transductive constrainedclustering algorithms. CLDPS is shown to outperform the state-of-theart constrained-clustering algorithms on multiple time-series datasets. An open-source implementation based on PyTorch is available 1 , which takes full advantage of GPU acceleration.

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LTSpAUC: Learning Time-series Shapelets for Optimizing Partial AUC

Cited by 6 publications

References 5 publications

LTSpAUC: Learning Time-Series Shapelets for Partial AUC Maximization

LTSpAUC: Learning Time-Series Shapelets for Partial AUC Maximization

Optimizing Two-Way Partial AUC With an End-to-End Framework

CDPS: Constrained DTW-Preserving Shapelets

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