16 citations · 38 across the 5 of their papers we have counts for
6 papers
Detecting Electric Devices in 3D Images of Bags
Anthony Bagnall, Paul Southam, James Large +1
The aviation and transport security industries face the challenge of screening high volumes of baggage for threats and contraband in the minimum time possible. Automation and semi-…
A tale of two toolkits, report the second: bake off redux. Chapter 1. dictionary based classifiers
Anthony Bagnall, James Large, Matthew Middlehurst
Time series classification (TSC) is the problem of learning labels from time dependent data. One class of algorithms is derived from a bag of words approach. A window is run along…
A tale of two toolkits, report the first: benchmarking time series classification algorithms for correctness and efficiency
Anthony Bagnall, Franz Király, Markus Löning +2
sktime is an open source, Python based, sklearn compatible toolkit for time series analysis developed by researchers at the University of East Anglia (UEA), University College Lond…
sktime: A Unified Interface for Machine Learning with Time Series
Markus Löning, Anthony Bagnall, Sajaysurya Ganesh +3
We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series. Time series data gives rise to various distinct bu…
A Shapelet Transform for Multivariate Time Series Classification
Aaron Bostrom, Anthony Bagnall
Shapelets are phase independent subsequences designed for time series classification. We propose three adaptations to the Shapelet Transform (ST) to capture multivariate features i…
On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms
Anthony Bagnall, Gavin C. Cawley
We demonstrate that, for a range of state-of-the-art machine learning algorithms, the differences in generalisation performance obtained using default parameter settings and using…