Dissimilarity-based representations for one-class classification on time series

Mauceri, Stefano; Sweeney, James; McDermott, James

doi:10.1016/j.patcog.2019.107122

Cited by 19 publications

(10 citation statements)

References 34 publications

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Section: Feature-based Tscmentioning

confidence: 99%

“…There are only few works investigating one-class TSC [37]. In a previous experimental study, we evaluate several approaches to derive dissimilarity-based representations for one-class TSC [37]. In dissimilarity-based representations, given a time series, each feature corresponds to its dissimilarity (distance) from one of a set of "reference" time series.…”

Section: Feature-based Tscmentioning

confidence: 99%

“…Other related applications are: fraud detection [44], machine fault detection [51], medical diagnosis [48], network intrusion [7], and signature verification [21]. This problem is little investigated in the context of TSC [37].…”

Section: Classification Frameworkmentioning

confidence: 99%

“…The classification performance of extracted features is evaluated using a oneclass classifier [37] (Sect. 3.4).…”

Section: Introductionmentioning

confidence: 99%

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Feature extraction by grammatical evolution for one-class time series classification

Mauceri

Sweeney

Nicolau

et al. 2021

Genet Program Evolvable Mach

Self Cite

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When dealing with a new time series classification problem, modellers do not know in advance which features could enable the best classification performance. We propose an evolutionary algorithm based on grammatical evolution to attain a data-driven feature-based representation of time series with minimal human intervention. The proposed algorithm can select both the features to extract and the sub-sequences from which to extract them. These choices not only impact classification performance but also allow understanding of the problem at hand. The algorithm is tested on 30 problems outperforming several benchmarks. Finally, in a case study related to subject authentication, we show how features learned for a given subject are able to generalise to subjects unseen during the extraction phase.

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Section: Feature-based Tscmentioning

confidence: 99%

Section: Feature-based Tscmentioning

confidence: 99%

Section: Classification Frameworkmentioning

confidence: 99%

“…The classification performance of extracted features is evaluated using a oneclass classifier [37] (Sect. 3.4).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Feature extraction by grammatical evolution for one-class time series classification

Mauceri

Sweeney

Nicolau

et al. 2021

Genet Program Evolvable Mach

Self Cite

View full text Add to dashboard Cite

show abstract

“…Subsequently, the authors used the kNN method on a PC space created by the MPCA [ 13 ]. As deep learning has made tremendous progress in recent years, Lee et al [ 14 ] proposed a convolutional neural network model that detects CVD process faults and finds fault-introducing process parameters.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection Using Signal Segmentation and One-Class Classification in Diffusion Process of Semiconductor Manufacturing

Chang

Yoo

Baek

2021

Sensors

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This paper proposes a new diagnostic method for sensor signals collected during semiconductor manufacturing. These signals provide important information for predicting the quality and yield of the finished product. Much of the data gathered during this process is time series data for fault detection and classification (FDC) in real time. This means that time series classification (TSC) must be performed during fabrication. With advances in semiconductor manufacturing, the distinction between normal and abnormal data has become increasingly significant as new challenges arise in their identification. One challenge is that an extremely high FDC performance is required, which directly impacts productivity and yield. However, general classification algorithms can have difficulty separating normal and abnormal data because of subtle differences. Another challenge is that the frequency of abnormal data is remarkably low. Hence, engineers can use only normal data to develop their models. This study presents a method that overcomes these problems and improves the FDC performance; it consists of two phases. Phase I has three steps: signal segmentation, feature extraction based on local outlier factors (LOF), and one-class classification (OCC) modeling using the isolation forest (iF) algorithm. Phase II, the test stage, consists of three steps: signal segmentation, feature extraction, and anomaly detection. The performance of the proposed method is superior to that of other baseline methods.

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Target Class Supervised Sample Length and Training Sample Reduction of Univariate Time Series

Sonbhadra

Agarwal

Nagabhushan

2021

Lecture Notes in Computer Science

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Dissimilarity-based representations for one-class classification on time series

Cited by 19 publications

References 34 publications

Feature extraction by grammatical evolution for one-class time series classification

Feature extraction by grammatical evolution for one-class time series classification

Anomaly Detection Using Signal Segmentation and One-Class Classification in Diffusion Process of Semiconductor Manufacturing

Target Class Supervised Sample Length and Training Sample Reduction of Univariate Time Series

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