A
Anthony J. Bagnall
Researcher at University of East Anglia
Publications - 113
Citations - 6853
Anthony J. Bagnall is an academic researcher from University of East Anglia. The author has contributed to research in topics: Dynamic time warping & Euclidean distance. The author has an hindex of 31, co-authored 107 publications receiving 4807 citations. Previous affiliations of Anthony J. Bagnall include Norwich University.
Papers
More filters
Posted Content
A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0.
TL;DR: An overview of the latest stable HIVE-COTE, version 1.0 is presented, a walkthrough guide of how to use the classifier is provided, and extensive experimental evaluation of its predictive performance and resource usage are conducted.
Proceedings Article
Ensembles of Elastic Distance Measures for Time Series Classification
Jason Lines,Anthony J. Bagnall +1 more
TL;DR: This work believes that its ensemble is the first ever classifier to significantly outperform Dynamic Time Warping and as such raises the bar for future work in this area.
Proceedings ArticleDOI
Game playing with autonomous adaptive agents in a simplified economic model of the UK market in electricity generation
TL;DR: In this paper, a simplified model of the UK market in electricity where autonomous adaptive agents representing electricity generation companies compete by bidding for the right to generate in a series of non-cooperative games simulating scenarios seen in the real world market is described.
Journal ArticleDOI
Ensembles of random sphere cover classifiers
Reda Younsi,Anthony J. Bagnall +1 more
TL;DR: A new set of ensemble methods for the randomised sphere cover (RSC) classifier are proposed and evaluated and it is demonstrated via a case study on six gene expression data sets that αRSSE can outperform other subspace ensemble methods on high dimensional data when used in conjunction with an attribute filter.
Journal Article
An adaptive agent model for generator company bidding in the UK power pool
TL;DR: An autonomous adaptive agent model of the UK market in electricity, where the agents represent electricity generating companies and the adaptive agent uses a hierarchical agent structure with two Learning Classifier Systems to evolve market bidding rules to meet two objectives.