| The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances A Bagnall, J Lines, A Bostrom, J Large, E Keogh Data mining and knowledge discovery 31, 606-660, 2017 | 1684 | 2017 |
| Time-series classification with COTE: the collective of transformation-based ensembles A Bagnall, J Lines, J Hills, A Bostrom IEEE Transactions on Knowledge and Data Engineering 27 (9), 2522-2535, 2015 | 563 | 2015 |
| The UEA multivariate time series classification archive, 2018 A Bagnall, HA Dau, J Lines, M Flynn, J Large, A Bostrom, P Southam, ... arXiv preprint arXiv:1811.00075, 2018 | 387 | 2018 |
| HIVE-COTE 2.0: a new meta ensemble for time series classification M Middlehurst, J Large, M Flynn, J Lines, A Bostrom, A Bagnall Machine Learning 110 (11), 3211-3243, 2021 | 181 | 2021 |
| What is cost-efficient phenotyping? Optimizing costs for different scenarios D Reynolds, F Baret, C Welcker, A Bostrom, J Ball, F Cellini, A Lorence, ... Plant Science, 2018 | 141 | 2018 |
| Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production A Bauer, AG Bostrom, J Ball, C Applegate, T Cheng, S Laycock, SM Rojas, ... Horticulture research 6, 2019 | 140 | 2019 |
| Binary shapelet transform for multiclass time series classification A Bostrom, A Bagnall Transactions on Large-Scale Data-and Knowledge-Centered Systems XXXII …, 2017 | 102 | 2017 |
| Binary shapelet transform for multiclass time series classification A Bostrom, A Bagnall Big Data Analytics and Knowledge Discovery: 17th International Conference …, 2015 | 98 | 2015 |
| SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination J Colmer, CM O'Neill, R Wells, A Bostrom, D Reynolds, D Websdale, ... New Phytologist 228 (2), 778-793, 2020 | 91 | 2020 |
| Is rotation forest the best classifier for problems with continuous features? A Bagnall, M Flynn, J Large, J Line, A Bostrom, G Cawley arXiv preprint arXiv:1809.06705, 2018 | 45 | 2018 |
| A shapelet transform for multivariate time series classification A Bostrom, A Bagnall arXiv preprint arXiv:1712.06428, 2017 | 34 | 2017 |
| Simulated data experiments for time series classification Part 1: accuracy comparison with default settings A Bagnall, A Bostrom, J Large, J Lines arXiv preprint arXiv:1703.09480, 2017 | 21 | 2017 |
| Evaluating Improvements to the Shapelet Transform A Bostrom, A Bagnall, J Lines | 11 | 2016 |
| Shapelet transforms for univariate and multivariate time series classification A Bostrom University of East Anglia, 2018 | 7 | 2018 |
| Lines J The UEA TSC codebase A Bagnall, A Bostrom | 6 | |
| Multiple imputation ensembles for time series (MIE-TS) A Aleryani, A Bostrom, W Wang, B Iglesia ACM Transactions on Knowledge Discovery from Data 17 (3), 1-28, 2023 | 3 | 2023 |
| AirSurf-Lettuce: an aerial image analysis platform for ultra-scale field phenotyping and precision agriculture using computer vision and deep learning A Bauer, AG Bostrom, J Ball, C Applegate, T Cheng, S Laycock, SM Rojas, ... bioRxiv, 527184, 2019 | 3 | 2019 |
| Time Series Classification Website A Bagnall, E Keogh, J Lines, A Bostrom, J Large | 3 | 2016 |
| A review and experimental evaluation of recent advances in time series classification A Bagnall, J Lines, A Bostrom, J Large | | |
Tony BagnallProfessor of Computer Science, University of East AngliaGeverifieerd e-mailadres voor uea.ac.uk
