Aaron Bostrom
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View article: Multiple Imputation Ensembles for Time Series (MIE-TS)
Multiple Imputation Ensembles for Time Series (MIE-TS) Open
Time series classification has become an interesting field of research, thanks to the extensive studies conducted in the past two decades. Time series may have missing data, which may affect both the representation and also modeling of tim…
View article: alan-turing-institute/sktime: v0.5.2
alan-turing-institute/sktime: v0.5.2 Open
What's New Fixed Fix ModuleNotFoundError issue (#613) @Hephaest Fixes _fit(X) in KNN (#610) @TonyBagnall UEA TSC module improvements 2 (#599) @TonyBagnall Fix sktime.classification.frequency_based not found error (#606) @Hephaest UEA TSC m…
View article: SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination
SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination Open
Summary Efficient seed germination and establishment are important traits for field and glasshouse crops. Large‐scale germination experiments are laborious and prone to observer errors, leading to the necessity for automated methods. We ex…
View article: Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production
Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production Open
Aerial imagery is regularly used by crop researchers, growers and farmers to monitor crops during the growing season. To extract meaningful information from large-scale aerial images collected from the field, high-throughput phenotypic ana…
View article: AirSurf-<i>Lettuce</i>: an aerial image analysis platform for ultra-scale field phenotyping and precision agriculture using computer vision and deep learning
AirSurf-<i>Lettuce</i>: an aerial image analysis platform for ultra-scale field phenotyping and precision agriculture using computer vision and deep learning Open
Aerial imagery is regularly used by farmers and growers to monitor crops during the growing season. To extract meaningful phenotypic information from large-scale aerial images collected regularly from the field, high-throughput analytic so…
View article: The UEA multivariate time series classification archive, 2018
The UEA multivariate time series classification archive, 2018 Open
In 2002, the UCR time series classification archive was first released with sixteen datasets. It gradually expanded, until 2015 when it increased in size from 45 datasets to 85 datasets. In October 2018 more datasets were added, bringing t…
View article: Is rotation forest the best classifier for problems with continuous features?
Is rotation forest the best classifier for problems with continuous features? Open
In short, our experiments suggest that yes, on average, rotation forest is better than the most common alternatives when all the attributes are real-valued. Rotation forest is a tree based ensemble that performs transforms on subsets of at…
View article: Shapelet Transforms for Univariate and Multivariate Time Series Classification
Shapelet Transforms for Univariate and Multivariate Time Series Classification Open
Time Series Classification (TSC) is a growing field of machine learning research. One particular algorithm from the TSC literature is the Shapelet Transform (ST). Shapelets are a phase independent subsequences that are extracted from times…
View article: A Shapelet Transform for Multivariate Time Series Classification
A Shapelet Transform for Multivariate Time Series Classification Open
Shapelets are phase independent subsequences designed for time series classification. We propose three adaptations to the Shapelet Transform (ST) to capture multivariate features in multivariate time series classification. We create a unif…
View article: Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings
Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings Open
There are now a broad range of time series classification (TSC) algorithms designed to exploit different representations of the data. These have been evaluated on a range of problems hosted at the UCR-UEA TSC Archive (www.timeseriesclassif…
View article: Simulated Data Experiments for Time Series Classification Part 1:\n Accuracy Comparison with Default Settings
Simulated Data Experiments for Time Series Classification Part 1:\n Accuracy Comparison with Default Settings Open
There are now a broad range of time series classification (TSC) algorithms\ndesigned to exploit different representations of the data. These have been\nevaluated on a range of problems hosted at the UCR-UEA TSC Archive\n(www.timeseriesclas…
View article: The Great Time Series Classification Bake Off: An Experimental\n Evaluation of Recently Proposed Algorithms. Extended Version
The Great Time Series Classification Bake Off: An Experimental\n Evaluation of Recently Proposed Algorithms. Extended Version Open
In the last five years there have been a large number of new time series\nclassification algorithms proposed in the literature. These algorithms have\nbeen evaluated on subsets of the 47 data sets in the University of California,\nRiversid…
View article: The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version
The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version Open
In the last five years there have been a large number of new time series classification algorithms proposed in the literature. These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside t…