A brief survey on sequence classification

By Zhengzheng Xing, Jian Pei, Eamonn J. Keogh. 2010

642 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Dilated Recurrent Neural Networks (2017, methodology): “Furthermore, in all the experiments, we apply the sequence classification setting [25], where the output layer only adds at the end of the sequence.”
Exploiting multi-channels deep convolutional neural networks for multivariate time series classification (2015, methodology): “distance based methods, traditional feature-based classification methods could also be applied for time series [1], and the performance of these methods depends on the quality of hand-crafted features heavily.”
Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks (2014, methodology): “Other than sequence distance based methods, feature-based classification methods [8] follow the traditional classification framework.”
Data Classification: Algorithms and Applications (2014, background): “A brief survey of time-series and sequence classification methods may be found in [91].”
Multivariate LSTM-FCNs for Time Series Classification (2018, background): “Typically, feature based classification algorithms rely heavily on the features being extracted from the time series data [11].”
Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings (2019, positive): “Examples are various version of Gaussian process models for predicting multivariate time series in continuous time, including those used for representing irregularly sampled clinical time series [20,23].”
Using dynamic time warping distances as features for improved time series classification (2016, methodology): “…that have been proposed for time series classification, the decades old dynamic time warping (DTW) in combination with one-nearest neighbor has not been easy to beat convincingly (Xi et al. 2006; Ding et al. 2008; Xing et al. 2010; Chen et al. 2013; Wang et al. 2013; Lines and Bagnall 2014).”
Outlier Analysis (2013, methodology): “This is known as early classification [475].”
Machine Learning and Decision Support in Critical Care (2016, background): “Time-series search has a broad range of applications from finance to medical informatics, however, robust algorithms for finding predictive patterns in long sequences of nonstationary multivariate time series are sparse [156].”
Multi-Scale Convolutional Neural Networks for Time Series Classification (2016, methodology): “Most existing TSC approaches fall into two categories [31]: distance-based methods and feature-based methods.”
Sequence Classification Restricted Boltzmann Machines With Gated Units (2020, background): “In [10] and [11], the terms conventional and strong are established, respectively, for these classiﬁcations for tasks.”