Machine learning for medical imaging: methodological failures and recommendations for the future
G Varoquaux, V Cheplygina - NPJ digital medicine, 2022 - nature.com
Research in computer analysis of medical images bears many promises to improve patients'
health. However, a number of systematic challenges are slowing down the progress of the …
health. However, a number of systematic challenges are slowing down the progress of the …
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
Abstract Time Series Classification (TSC) involves building predictive models for a discrete
target variable from ordered, real valued, attributes. Over recent years, a new set of TSC …
target variable from ordered, real valued, attributes. Over recent years, a new set of TSC …
Minirocket: A very fast (almost) deterministic transform for time series classification
Rocket achieves state-of-the-art accuracy for time series classification with a fraction of the
computational expense of most existing methods by transforming input time series using …
computational expense of most existing methods by transforming input time series using …
Inceptiontime: Finding alexnet for time series classification
This paper brings deep learning at the forefront of research into time series classification
(TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of …
(TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of …
ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels
Most methods for time series classification that attain state-of-the-art accuracy have high
computational complexity, requiring significant training time even for smaller datasets, and …
computational complexity, requiring significant training time even for smaller datasets, and …
HIVE-COTE 2.0: a new meta ensemble for time series classification
Abstract The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE)
is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its …
is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its …
Deep learning for time series classification: a review
Abstract 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 …
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
The UCR time series archive
The UCR time series archive–introduced in 2002, has become an important resource in the
time series data mining community, with at least one thousand published papers making use …
time series data mining community, with at least one thousand published papers making use …
Bake off redux: a review and experimental evaluation of recent time series classification algorithms
In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31 (3): 606-
660.) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the …
660.) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the …
A data-characteristic-aware latent factor model for web services QoS prediction
How to accurately predict unknown quality-of-service (QoS) data based on observed ones is
a hot yet thorny issue in Web service-related applications. Recently, a latent factor (LF) …
a hot yet thorny issue in Web service-related applications. Recently, a latent factor (LF) …