5,229 research outputs found
Deep learning for time series classification: a review
Time Series Classification (TSC) is an important and challenging problem in
data mining. With the increase of time series data availability, hundreds of
TSC algorithms have been proposed. Among these methods, only a few have
considered Deep Neural Networks (DNNs) to perform this task. This is surprising
as deep learning has seen very successful applications in the last years. DNNs
have indeed revolutionized the field of computer vision especially with the
advent of novel deeper architectures such as Residual and Convolutional Neural
Networks. Apart from images, sequential data such as text and audio can also be
processed with DNNs to reach state-of-the-art performance for document
classification and speech recognition. In this article, we study the current
state-of-the-art performance of deep learning algorithms for TSC by presenting
an empirical study of the most recent DNN architectures for TSC. We give an
overview of the most successful deep learning applications in various time
series domains under a unified taxonomy of DNNs for TSC. We also provide an
open source deep learning framework to the TSC community where we implemented
each of the compared approaches and evaluated them on a univariate TSC
benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By
training 8,730 deep learning models on 97 time series datasets, we propose the
most exhaustive study of DNNs for TSC to date.Comment: Accepted at Data Mining and Knowledge Discover
Convolutional Neural Networks for Epileptic Seizure Prediction
Epilepsy is the most common neurological disorder and an accurate forecast of
seizures would help to overcome the patient's uncertainty and helplessness. In
this contribution, we present and discuss a novel methodology for the
classification of intracranial electroencephalography (iEEG) for seizure
prediction. Contrary to previous approaches, we categorically refrain from an
extraction of hand-crafted features and use a convolutional neural network
(CNN) topology instead for both the determination of suitable signal
characteristics and the binary classification of preictal and interictal
segments. Three different models have been evaluated on public datasets with
long-term recordings from four dogs and three patients. Overall, our findings
demonstrate the general applicability. In this work we discuss the strengths
and limitations of our methodology.Comment: accepted for MLESP 201
Evaluating surgical skills from kinematic data using convolutional neural networks
The need for automatic surgical skills assessment is increasing, especially
because manual feedback from senior surgeons observing junior surgeons is prone
to subjectivity and time consuming. Thus, automating surgical skills evaluation
is a very important step towards improving surgical practice. In this paper, we
designed a Convolutional Neural Network (CNN) to evaluate surgeon skills by
extracting patterns in the surgeon motions performed in robotic surgery. The
proposed method is validated on the JIGSAWS dataset and achieved very
competitive results with 100% accuracy on the suturing and needle passing
tasks. While we leveraged from the CNNs efficiency, we also managed to mitigate
its black-box effect using class activation map. This feature allows our method
to automatically highlight which parts of the surgical task influenced the
skill prediction and can be used to explain the classification and to provide
personalized feedback to the trainee.Comment: Accepted at MICCAI 201
An Expectation Maximization Method to Learn the Group Structure of Deep Neural Network
Department of Computer Science and EngineeringAnalyzing multivariate time series data is important for many applications such as automated control, sensor fault diagnosis and financial data analysis. One of the key challenges is to learn latent features automatically from dynamically changing multivariate input. Convolutional neural networks (CNNs) have been successful to learn generalized feature extractors with shared parameters over the spatial domain in visual recognition tasks. For high-dimensional multivariate time series, designing an appropriate CNN model structure is challenging because the kernels may need to be extended through the full dimension of the input volume. To address this issue, we propose an Expectation Maximization (EM) method to learn the group structure of deep neural networks so that we can process the multiple high-dimensional kernels efficiently. This algorithm groups the kernels for each channel using the EM method and partition the kernel matrix into a block matrix. The EM method assumes the Gaussian Mixture Model (GMM) and the parameters of the GMM is updated together with the parameters of deep neural network by end-to-end backpropagation learning.ope
MTS2Graph: Interpretable Multivariate Time Series Classification with Temporal Evolving Graphs
Conventional time series classification approaches based on bags of patterns
or shapelets face significant challenges in dealing with a vast amount of
feature candidates from high-dimensional multivariate data. In contrast, deep
neural networks can learn low-dimensional features efficiently, and in
particular, Convolutional Neural Networks (CNN) have shown promising results in
classifying Multivariate Time Series (MTS) data. A key factor in the success of
deep neural networks is this astonishing expressive power. However, this power
comes at the cost of complex, black-boxed models, conflicting with the goals of
building reliable and human-understandable models. An essential criterion in
understanding such predictive deep models involves quantifying the contribution
of time-varying input variables to the classification. Hence, in this work, we
introduce a new framework for interpreting multivariate time series data by
extracting and clustering the input representative patterns that highly
activate CNN neurons. This way, we identify each signal's role and
dependencies, considering all possible combinations of signals in the MTS
input. Then, we construct a graph that captures the temporal relationship
between the extracted patterns for each layer. An effective graph merging
strategy finds the connection of each node to the previous layer's nodes.
Finally, a graph embedding algorithm generates new representations of the
created interpretable time-series features. To evaluate the performance of our
proposed framework, we run extensive experiments on eight datasets of the
UCR/UEA archive, along with HAR and PAM datasets. The experiments indicate the
benefit of our time-aware graph-based representation in MTS classification
while enriching them with more interpretability
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