Search Results for author: Anthony Bagnall

Found 23 papers, 3 papers with code

HIVE-COTE 2.0: a new meta ensemble for time series classification

no code implementations15 Apr 2021 Matthew Middlehurst, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, Anthony Bagnall

Since it was first proposed in 2016, the algorithm has remained state of the art for accuracy on the UCR time series classification archive.

Classification General Classification +2

Detecting Electric Devices in 3D Images of Bags

no code implementations25 Apr 2020 Anthony Bagnall, Paul Southam, James Large, Richard Harvey

Given the massive volume of luggage that needs to be screened for this threat, the best way to automate the detection is to first filter whether a bag contains an electric device or not, and if it does, to identify the number of devices and their location.

Computed Tomography (CT)

A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0

no code implementations13 Apr 2020 Anthony Bagnall, Michael Flynn, James Large, Jason Lines, Matthew Middlehurst

The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification.

Time Series Time Series Classification

sktime: A Unified Interface for Machine Learning with Time Series

no code implementations17 Sep 2019 Markus Löning, Anthony Bagnall, Sajaysurya Ganesh, Viktor Kazakov, Jason Lines, Franz J. Király

We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series.

Time Series Time Series Analysis +2

Scalable Dictionary Classifiers for Time Series Classification

1 code implementation26 Jul 2019 Matthew Middlehurst, William Vickers, Anthony Bagnall

Dictionary based classifiers are a family of algorithms for time series classification (TSC), that focus on capturing the frequency of pattern occurrences in a time series.

Classification General Classification +2

From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers

no code implementations18 Sep 2018 James Large, Anthony Bagnall, Simon Malinowski, Romain Tavenard

We find that whilst ensembling is a key component for both algorithms, the effect of the other components is mixed and more complex.

General Classification Image Classification +2

The Heterogeneous Ensembles of Standard Classification Algorithms (HESCA): the Whole is Greater than the Sum of its Parts

no code implementations25 Oct 2017 James Large, Jason Lines, Anthony Bagnall

We show that the Heterogeneous Ensembles of Standard Classification Algorithms (HESCA), which ensembles based on error estimates formed on the train data, is significantly better (in terms of error, balanced error, negative log likelihood and area under the ROC curve) than its individual components, picking the component that is best on train data, and a support vector machine tuned over 1089 different parameter configurations.

Decision Making General Classification

Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings

no code implementations28 Mar 2017 Anthony Bagnall, Aaron Bostrom, James Large, Jason Lines

We describe what results we expected from each class of algorithm and data representation, then observe whether these prior beliefs are supported by the experimental evidence.

General Classification Time Series +1

On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms

no code implementations20 Mar 2017 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 parameters tuned via cross-validation can be similar in magnitude to the differences in performance observed between state-of-the-art and uncompetitive learning systems.

General Classification Model Selection

The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version

no code implementations4 Feb 2016 Anthony Bagnall, Aaron Bostrom, James Large, Jason Lines

These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside time series classification archive.

General Classification Time Series +1

Ensembles of Random Sphere Cover Classifiers

no code implementations17 Sep 2014 Anthony Bagnall, Reda Younsi

We propose two ensemble methods tailored to the RSC classifier; $\alpha \beta$RSE, an ensemble based on instance resampling and $\alpha$RSSE, a subspace ensemble.

Finding Motif Sets in Time Series

no code implementations14 Jul 2014 Anthony Bagnall, Jon Hills, Jason Lines

Two are greedy algorithms based on pairwise comparison, and the third uses a heuristic measure of set quality to find the motif set directly.

Time Series

Predictive Modelling of Bone Age through Classification and Regression of Bone Shapes

no code implementations18 Jun 2014 Anthony Bagnall, Luke Davis

Our approach to automated bone age assessment is to modularise the algorithm into the following three stages: segment and verify hand outline; segment and verify bones; use the bone outlines to construct models of age.

General Classification

An Experimental Evaluation of Nearest Neighbour Time Series Classification

no code implementations18 Jun 2014 Anthony Bagnall, Jason Lines

Specifically, we compare 1-NN classifiers with Euclidean and DTW distance to standard classifiers, examine whether the performance of 1-NN Euclidean approaches that of 1-NN DTW as the number of cases increases, assess whether there is any benefit of setting $k$ for $k$-NN through cross validation whether it is worth setting the warping path for DTW through cross validation and finally is it better to use a window or weighting for DTW.

Classification Dynamic Time Warping +3

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