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ORCIDAnthony J. Bagnall
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- affiliation: University of East Anglia, School of Computing Sciences, Norwich, UK
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2020 – today
- 2024
[j30]Matthew Middlehurst, Patrick Schäfer, Anthony J. Bagnall:
Bake off redux: a review and experimental evaluation of recent time series classification algorithms. Data Min. Knowl. Discov. 38(4): 1958-2031 (2024)- [j29]David Guijo-Rubio, Matthew Middlehurst, Guilherme Arcencio, Diego Furtado Silva, Anthony J. Bagnall:
Unsupervised feature based algorithms for time series extrinsic regression. Data Min. Knowl. Discov. 38(4): 2141-2185 (2024) - [j28]Christopher Holder
, Matthew Middlehurst, Anthony J. Bagnall:
A review and evaluation of elastic distance functions for time series clustering. Knowl. Inf. Syst. 66(2): 765-809 (2024) - [c51]Anthony J. Bagnall, Matthew Middlehurst, Germain Forestier, Ali Ismail-Fawaz, Antoine Guillaume, David Guijo-Rubio, Chang Wei Tan, Angus Dempster, Geoffrey I. Webb:
A Hands-on Introduction to Time Series Classification and Regression. KDD 2024: 6410-6411 - [i31]Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume, Christopher Holder, David Guijo-Rubio, Guzal Bulatova, Leonidas Tsaprounis, Lukasz Mentel, Martin Walter, Patrick Schäfer, Anthony J. Bagnall:
aeon: a Python toolkit for learning from time series. CoRR abs/2406.14231 (2024) - 2023
- [c50]Arik Ermshaus, Patrick Schäfer, Anthony J. Bagnall, Thomas Guyet, Georgiana Ifrim, Vincent Lemaire, Ulf Leser, Colin Leverger, Simon Malinowski:
Human Activity Segmentation Challenge @ ECML/PKDD'23. AALTD@ECML/PKDD 2023: 3-13 - [c49]Christopher Holder, David Guijo-Rubio, Anthony J. Bagnall:
Clustering Time Series with k-Medoids Based Algorithms. AALTD@ECML/PKDD 2023: 39-55 - [c48]Matthew Middlehurst, Anthony J. Bagnall:
Extracting Features from Random Subseries: A Hybrid Pipeline for Time Series Classification and Extrinsic Regression. AALTD@ECML/PKDD 2023: 113-126 - [c47]Christopher Holder, David Guijo-Rubio, Anthony J. Bagnall:
Barycentre Averaging for the Move-Split-Merge Time Series Distance Measure. KDIR 2023: 51-62 - [c46]Aiden Rushbrooke, Jordan Tsigarides, Saber Sami, Anthony J. Bagnall:
Time Series Classification of Electroencephalography Data. IWANN (1) 2023: 601-613 - [e5]Thomas Guyet, Georgiana Ifrim, Simon Malinowski, Anthony J. Bagnall, Patrick Schäfer, Vincent Lemaire:
Advanced Analytics and Learning on Temporal Data - 7th ECML PKDD Workshop, AALTD 2022, Grenoble, France, September 19-23, 2022, Revised Selected Papers. Lecture Notes in Computer Science 13812, Springer 2023, ISBN 978-3-031-24377-6 [contents] - [e4]Georgiana Ifrim, Romain Tavenard, Anthony J. Bagnall, Patrick Schäfer, Simon Malinowski, Thomas Guyet, Vincent Lemaire:
Advanced Analytics and Learning on Temporal Data - 8th ECML PKDD Workshop, AALTD 2023, Turin, Italy, September 18-22, 2023, Revised Selected Papers. Lecture Notes in Computer Science 14343, Springer 2023, ISBN 978-3-031-49895-4 [contents] - [i30]Matthew Middlehurst, Patrick Schäfer, Anthony J. Bagnall:
Bake off redux: a review and experimental evaluation of recent time series classification algorithms. CoRR abs/2304.13029 (2023) - [i29]David Guijo-Rubio, Matthew Middlehurst, Guilherme Arcencio, Diego Furtado Silva, Anthony J. Bagnall:
Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression. CoRR abs/2305.01429 (2023) - [i28]Rafael Ayllón-Gavilán, David Guijo-Rubio, Pedro Antonio Gutiérrez, Anthony J. Bagnall, César Hervás-Martínez:
Convolutional and Deep Learning based techniques for Time Series Ordinal Classification. CoRR abs/2306.10084 (2023) - 2022
- [c45]Alejandro Pasos Ruiz, Anthony J. Bagnall:
Dimension Selection Strategies for Multivariate Time Series Classification with HIVE-COTEv2.0. AALTD@ECML/PKDD 2022: 133-147 - [c44]Matthew Middlehurst, Anthony J. Bagnall:
The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier. ICPRAI (2) 2022: 150-161 - [i27]Matthew Middlehurst, Anthony J. Bagnall:
The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier. CoRR abs/2201.12048 (2022) - [i26]Christopher Holder, Matthew Middlehurst, Anthony J. Bagnall:
A Review and Evaluation of Elastic Distance Functions for Time Series Clustering. CoRR abs/2205.15181 (2022) - 2021
- [j27]Alejandro Pasos Ruiz, Michael Flynn, James Large, Matthew Middlehurst, Anthony J. Bagnall:
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Discov. 35(2): 401-449 (2021) - [j26]Matthew Middlehurst, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, Anthony J. Bagnall:
HIVE-COTE 2.0: a new meta ensemble for time series classification. Mach. Learn. 110(11): 3211-3243 (2021) - [e3]Vincent Lemaire, Simon Malinowski, Anthony J. Bagnall, Thomas Guyet, Romain Tavenard, Georgiana Ifrim:
Advanced Analytics and Learning on Temporal Data - 6th ECML PKDD Workshop, AALTD 2021, Bilbao, Spain, September 13, 2021, Revised Selected Papers. Lecture Notes in Computer Science 13114, Springer 2021, ISBN 978-3-030-91444-8 [contents] - [i25]Matthew Middlehurst, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, Anthony J. Bagnall:
HIVE-COTE 2.0: a new meta ensemble for time series classification. CoRR abs/2104.07551 (2021) - [i24]Matthew Middlehurst, James Large, Gavin C. Cawley, Anthony J. Bagnall:
The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification. CoRR abs/2105.03841 (2021) - 2020
- [j25]Shaghayegh Gharghabi, Shima Imani, Anthony J. Bagnall, Amirali Darvishzadeh, Eamonn J. Keogh:
An ultra-fast time series distance measure to allow data mining in more complex real-world deployments. Data Min. Knowl. Discov. 34(4): 1104-1135 (2020) - [c43]Matthew Middlehurst, James Large, Anthony J. Bagnall:
The Canonical Interval Forest (CIF) Classifier for Time Series Classification. IEEE BigData 2020: 188-195 - [c42]David Guijo-Rubio, Pedro Antonio Gutiérrez, Anthony J. Bagnall, César Hervás-Martínez:
Time series ordinal classification via shapelets. IJCNN 2020: 1-8 - [c41]Anthony J. Bagnall, Michael Flynn, James Large, Jason Lines, Matthew Middlehurst:
On the Usage and Performance of the Hierarchical Vote Collective of Transformation-Based Ensembles Version 1.0 (HIVE-COTE v1.0). AALTD@PKDD/ECML 2020: 3-18 - [c40]David Guijo-Rubio, Pedro Antonio Gutiérrez, Anthony J. Bagnall, César Hervás-Martínez:
Ordinal Versus Nominal Time Series Classification. AALTD@PKDD/ECML 2020: 19-29 - [c39]Matthew Middlehurst, James Large, Gavin C. Cawley, Anthony J. Bagnall:
The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification. ECML/PKDD (1) 2020: 660-676 - [c38]Thakolpat Khampuengson, Anthony J. Bagnall, Wenjia Wang:
Developing Ensemble Methods for Detecting Anomalies in Water Level Data. SGAI Conf. 2020: 145-151 - [e2]Vincent Lemaire, Simon Malinowski, Anthony J. Bagnall, Alexis Bondu, Thomas Guyet, Romain Tavenard:
Advanced Analytics and Learning on Temporal Data - 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers. Lecture Notes in Computer Science 11986, Springer 2020, ISBN 978-3-030-39097-6 [contents] - [e1]Vincent Lemaire, Simon Malinowski, Anthony J. Bagnall, Thomas Guyet, Romain Tavenard, Georgiana Ifrim:
Advanced Analytics and Learning on Temporal Data - 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers. Lecture Notes in Computer Science 12588, Springer 2020, ISBN 978-3-030-65741-3 [contents] - [i23]Anthony J. Bagnall, Michael Flynn, James Large, Jason Lines, Matthew Middlehurst:
A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0. CoRR abs/2004.06069 (2020) - [i22]Anthony J. Bagnall, Paul Southam, James Large, Richard W. Harvey:
Detecting Electric Devices in 3D Images of Bags. CoRR abs/2005.02163 (2020) - [i21]Alejandro Pasos Ruiz, Michael Flynn, Anthony J. Bagnall:
Benchmarking Multivariate Time Series Classification Algorithms. CoRR abs/2007.13156 (2020) - [i20]Matthew Middlehurst, James Large, Anthony J. Bagnall:
The Canonical Interval Forest (CIF) Classifier for Time Series Classification. CoRR abs/2008.09172 (2020)
2010 – 2019
- 2019
- [j24]James Large, Jason Lines, Anthony J. Bagnall:
A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates. Data Min. Knowl. Discov. 33(6): 1674-1709 (2019) - [j23]James Large, Anthony J. Bagnall, Simon Malinowski, Romain Tavenard:
On time series classification with dictionary-based classifiers. Intell. Data Anal. 23(5): 1073-1089 (2019) - [j22]Hoang Anh Dau, Anthony J. Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, Eamonn J. Keogh:
The UCR time series archive. IEEE CAA J. Autom. Sinica 6(6): 1293-1305 (2019) - [c37]James Large, Paul Southam, Anthony J. Bagnall:
Can Automated Smoothing Significantly Improve Benchmark Time Series Classification Algorithms? HAIS 2019: 50-60 - [c36]Matthew Middlehurst, William Vickers, Anthony J. Bagnall:
Scalable Dictionary Classifiers for Time Series Classification. IDEAL (1) 2019: 11-19 - [c35]James Large, Anthony J. Bagnall:
Mixing Hetero- and Homogeneous Models in Weighted Ensembles. IDEAL (1) 2019: 129-136 - [c34]David Guijo-Rubio, Pedro Antonio Gutiérrez, Romain Tavenard, Anthony J. Bagnall:
A Hybrid Approach to Time Series Classification with Shapelets. IDEAL (1) 2019: 137-144 - [c33]Michael Flynn, Anthony J. Bagnall:
Classifying Flies Based on Reconstructed Audio Signals. IDEAL (2) 2019: 249-258 - [i19]Matthew Middlehurst, William Vickers, Anthony J. Bagnall:
Scalable Dictionary Classifiers for Time Series Classification. CoRR abs/1907.11815 (2019) - [i18]Anthony J. Bagnall, Franz J. Király, Markus Löning, Matthew Middlehurst, George Oastler:
A tale of two toolkits, report the first: benchmarking time series classification algorithms for correctness and efficiency. CoRR abs/1909.05738 (2019) - [i17]Markus Löning, Anthony J. Bagnall, Sajaysurya Ganesh, Viktor Kazakov, Jason Lines, Franz J. Király:
sktime: A Unified Interface for Machine Learning with Time Series. CoRR abs/1909.07872 (2019) - [i16]Anthony J. Bagnall, James Large, Matthew Middlehurst:
A tale of two toolkits, report the second: bake off redux. Chapter 1. dictionary based classifiers. CoRR abs/1911.12008 (2019) - [i15]Anthony J. Bagnall, Richard L. Cole, Themis Palpanas, Konstantinos Zoumpatianos:
Data Series Management (Dagstuhl Seminar 19282). Dagstuhl Reports 9(7): 24-39 (2019) - 2018
- [j21]Hoang Anh Dau, Diego Furtado Silva, François Petitjean, Germain Forestier, Anthony J. Bagnall, Abdullah Mueen, Eamonn J. Keogh:
Optimizing dynamic time warping's window width for time series data mining applications. Data Min. Knowl. Discov. 32(4): 1074-1120 (2018) - [j20]Jason Lines, Sarah Taylor, Anthony J. Bagnall:
Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles. ACM Trans. Knowl. Discov. Data 12(5): 52:1-52:35 (2018) - [c32]Shaghayegh Gharghabi, Shima Imani, Anthony J. Bagnall, Amirali Darvishzadeh, Eamonn J. Keogh:
Matrix Profile XII: MPdist: A Novel Time Series Distance Measure to Allow Data Mining in More Challenging Scenarios. ICDM 2018: 965-970 - [c31]James Large, E. Kate Kemsley, Nikolaus Wellner, Ian Goodall, Anthony J. Bagnall:
Detecting Forged Alcohol Non-invasively Through Vibrational Spectroscopy and Machine Learning. PAKDD (1) 2018: 298-309 - [i14]Anthony J. Bagnall, Aaron Bostrom, Gavin C. Cawley, Michael Flynn, James Large, Jason Lines:
Is rotation forest the best classifier for problems with continuous features? CoRR abs/1809.06705 (2018) - [i13]James Large, Anthony J. Bagnall, Simon Malinowski, Romain Tavenard:
From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers. CoRR abs/1809.06751 (2018) - [i12]Hoang Anh Dau, Anthony J. Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, Eamonn J. Keogh:
The UCR Time Series Archive. CoRR abs/1810.07758 (2018) - [i11]Anthony J. Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, Eamonn J. Keogh:
The UEA multivariate time series classification archive, 2018. CoRR abs/1811.00075 (2018) - [i10]James Large, Paul Southam, Anthony J. Bagnall:
Can automated smoothing significantly improve benchmark time series classification algorithms? CoRR abs/1811.00894 (2018) - 2017
- [j19]Anthony J. Bagnall, Jason Lines, Aaron Bostrom, James Large, Eamonn J. Keogh:
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Discov. 31(3): 606-660 (2017) - [j18]Aaron Bostrom, Anthony J. Bagnall:
Binary Shapelet Transform for Multiclass Time Series Classification. Trans. Large Scale Data Knowl. Centered Syst. 32: 24-46 (2017) - [c30]Hoang Anh Dau, Diego Furtado Silva, François Petitjean, Germain Forestier, Anthony J. Bagnall, Eamonn J. Keogh:
Judicious setting of Dynamic Time Warping's window width allows more accurate classification of time series. IEEE BigData 2017: 917-922 - [i9]Anthony J. Bagnall, Gavin C. Cawley:
On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms. CoRR abs/1703.06777 (2017) - [i8]Anthony J. Bagnall, Aaron Bostrom, James Large, Jason Lines:
Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings. CoRR abs/1703.09480 (2017) - [i7]James Large, Jason Lines, Anthony J. Bagnall:
The Heterogeneous Ensembles of Standard Classification Algorithms (HESCA): the Whole is Greater than the Sum of its Parts. CoRR abs/1710.09220 (2017) - [i6]Aaron Bostrom, Anthony J. Bagnall:
A Shapelet Transform for Multivariate Time Series Classification. CoRR abs/1712.06428 (2017) - 2016
- [j17]Reda Younsi, Anthony J. Bagnall:
Ensembles of random sphere cover classifiers. Pattern Recognit. 49: 213-225 (2016) - [c29]Anthony J. Bagnall, Jason Lines, Jon Hills, Aaron Bostrom:
Time-series classification with COTE: The collective of transformation-based ensembles. ICDE 2016: 1548-1549 - [c28]Jason Lines, Sarah Taylor, Anthony J. Bagnall:
HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles for Time Series Classification. ICDM 2016: 1041-1046 - [i5]Anthony J. Bagnall, Aaron Bostrom, James Large, Jason Lines:
The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version. CoRR abs/1602.01711 (2016) - 2015
- [j16]Jason Lines, Anthony J. Bagnall:
Time series classification with ensembles of elastic distance measures. Data Min. Knowl. Discov. 29(3): 565-592 (2015) - [j15]Anthony J. Bagnall, Jason Lines, Jon Hills, Aaron Bostrom:
Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles. IEEE Trans. Knowl. Data Eng. 27(9): 2522-2535 (2015) - [c27]Aaron Bostrom, Anthony J. Bagnall:
Binary Shapelet Transform for Multiclass Time Series Classification. DaWaK 2015: 257-269 - [c26]Awat A. Saeed, Gavin C. Cawley, Anthony J. Bagnall:
Benchmarking the semi-supervised naïve Bayes classifier. IJCNN 2015: 1-8 - 2014
- [j14]Anthony J. Bagnall, Gareth J. Janacek:
A Run Length Transformation for Discriminating Between Auto Regressive Time Series. J. Classif. 31(2): 154-178 (2014) - [j13]Jon Hills, Jason Lines, Edgaras Baranauskas, James Mapp, Anthony J. Bagnall:
Classification of time series by shapelet transformation. Data Min. Knowl. Discov. 28(4): 851-881 (2014) - [c25]Jason Lines, Anthony J. Bagnall:
Ensembles of Elastic Distance Measures for Time Series Classification. SDM 2014: 524-532 - [i4]Anthony J. Bagnall, Jason Lines:
An Experimental Evaluation of Nearest Neighbour Time Series Classification. CoRR abs/1406.4757 (2014) - [i3]Anthony J. Bagnall, Luke M. Davis:
Predictive Modelling of Bone Age through Classification and Regression of Bone Shapes. CoRR abs/1406.4781 (2014) - [i2]Anthony J. Bagnall, Jon Hills, Jason Lines:
Finding Motif Sets in Time Series. CoRR abs/1407.3685 (2014) - [i1]Anthony J. Bagnall, Reda Younsi:
Ensembles of Random Sphere Cover Classifiers. CoRR abs/1409.4936 (2014) - 2013
- [j12]Jon Hills, Anthony J. Bagnall, Beatriz de la Iglesia, Graeme Richards:
BruteSuppression: a size reduction method for Apriori rule sets. J. Intell. Inf. Syst. 40(3): 431-454 (2013) - [c24]James Mapp, Mark Fisher, Anthony J. Bagnall, Jason Lines, Sally Warne, Joe Scutt Phillips:
Clupea Harengus: Intraspecies Distinction using Curvature Scale Space and Shapelets - Classification of North-sea and Thames Herring using Boundary Contour of Sagittal Otoliths. ICPRAM 2013: 138-143 - 2012
- [j11]Reda Younsi, Anthony J. Bagnall:
An efficient randomised sphere cover classifier. Int. J. Data Min. Model. Manag. 4(2): 156-171 (2012) - [j10]Luke M. Davis, Barry-John Theobald, Jason Lines, Andoni Toms, Anthony J. Bagnall:
On the Segmentation and Classification of Hand Radiographs. Int. J. Neural Syst. 22(5) (2012) - [c23]Luke M. Davis, Barry-John Theobald, Anthony J. Bagnall:
Automated Bone Age Assessment Using Feature Extraction. IDEAL 2012: 43-51 - [c22]Jon Hills, Luke M. Davis, Anthony J. Bagnall:
Interestingness Measures for Fixed Consequent Rules. IDEAL 2012: 68-75 - [c21]Jason Lines, Anthony J. Bagnall:
Alternative Quality Measures for Time Series Shapelets. IDEAL 2012: 475-483 - [c20]Jason Lines, Luke M. Davis, Jon Hills, Anthony J. Bagnall:
A shapelet transform for time series classification. KDD 2012: 289-297 - [c19]Anthony J. Bagnall, Luke M. Davis, Jon Hills, Jason Lines:
Transformation Based Ensembles for Time Series Classification. SDM 2012: 307-318 - 2011
- [c18]Luke M. Davis, Barry-John Theobald, Andoni Toms, Anthony J. Bagnall:
On the Extraction and Classification of Hand Outlines. IDEAL 2011: 92-99 - [c17]Jason Lines, Anthony J. Bagnall, Patrick Caiger-Smith, Simon Anderson:
Classification of Household Devices by Electricity Usage Profiles. IDEAL 2011: 403-412 - 2010
- [c16]Reda Younsi, Anthony J. Bagnall:
A Randomized Sphere Cover Classifier. IDEAL 2010: 234-241
2000 – 2009
- 2009
- [j9]Zhanna V. Zatuchna, Anthony J. Bagnall:
Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception. Adapt. Behav. 17(1): 28-57 (2009) - [j8]Zhanna V. Zatuchna, Anthony J. Bagnall:
A learning classifier system for mazes with aliasing clones. Nat. Comput. 8(1): 57-99 (2009) - 2008
- [c15]Anthony J. Bagnall, Simon Moxon, David J. Studholme, Vincent Moulton:
Time Series Data Mining Algorithms for Identifying Short RNA in Arabidopsis thaliana. BIOCOMP 2008: 182-188 - 2007
- [j7]Anthony J. Bagnall, Gavin C. Cawley, Ian M. Whittley, Larry Bull, Matthew Studley, Mike Pettipher, Firat Tekiner:
Super Computer Heterogeneous Classifier Meta-Ensembles. Int. J. Data Warehous. Min. 3(2): 67-82 (2007) - [j6]Larry Bull, Matthew Studley, Anthony J. Bagnall, Ian M. Whittley:
Learning Classifier System Ensembles With Rule-Sharing. IEEE Trans. Evol. Comput. 11(4): 496-502 (2007) - [c14]Iain Toft, Anthony J. Bagnall:
Adaptive Sniping for Volatile and Stable Continuous Double Auction Markets. AMEC/TADA 2007: 119-134 - 2006
- [j5]Anthony J. Bagnall, Iain Toft:
Autonomous Adaptive Agents for Single Seller Sealed Bid Auctions. Auton. Agents Multi Agent Syst. 12(3): 259-292 (2006) - [j4]Anthony J. Bagnall, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh, Stefano Lonardi, Gareth J. Janacek:
A Bit Level Representation for Time Series Data Mining with Shape Based Similarity. Data Min. Knowl. Discov. 13(1): 11-40 (2006) - [c13]Anthony J. Bagnall, Ian M. Whittley, Gareth J. Janacek, Kate Kemsley, Matthew Studley, Larry Bull:
A Comparison of DWT/PAA and DFT for Time Series Classification. DMIN 2006: 403-409 - [c12]Anthony J. Bagnall, Ian M. Whittley, Matthew Studley, Mike Pettipher, Firat Tekiner, Larry Bull:
Variance Stabilizing Regression Ensembles for Environmental Models. IJCNN 2006: 5355-5361 - 2005
- [j3]Anthony J. Bagnall, Gareth J. Janacek:
Clustering Time Series with Clipped Data. Mach. Learn. 58(2-3): 151-178 (2005) - [j2]Anthony J. Bagnall, George D. Smith:
A multiagent model of the UK market in electricity generation. IEEE Trans. Evol. Comput. 9(5): 522-536 (2005) - [c11]Larry Bull, Matthew Studley, Anthony J. Bagnall, Ian M. Whittley:
On the use of rule-sharing in learning classifier system ensembles. Congress on Evolutionary Computation 2005: 612-617 - [c10]Zhanna V. Zatuchna, Anthony J. Bagnall:
AgentP classifier system: self-adjusting vs. gradual approach. Congress on Evolutionary Computation 2005: 1196-1203 - [c9]Gareth J. Janacek, Anthony J. Bagnall, M. Powell:
A Likelihood Ratio Distance Measure for the Similarity Between the Fourier Transform of Time Series. PAKDD 2005: 737-743 - [c8]Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh, Anthony J. Bagnall, Stefano Lonardi:
A Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering. PAKDD 2005: 771-777 - 2004
- [c7]Abdul Aziz Gill, George D. Smith, Anthony J. Bagnall:
Improving Decision Tree Performance Through Induction- and Cluster-Based Stratified Sampling. IDEAL 2004: 339-344 - [c6]Anthony J. Bagnall, Gareth J. Janacek:
Clustering time series from ARMA models with clipped data. KDD 2004: 49-58 - 2003
- [c5]Anthony J. Bagnall, Iain Toft:
An Agent Model for First Price and Second Price Private Value Auctions. Artificial Evolution 2003: 281-292 - [c4]Beatriz de la Iglesia, M. S. Philpott, Anthony J. Bagnall, Victor J. Rayward-Smith:
Data mining rules using multi-objective evolutionary algorithms. IEEE Congress on Evolutionary Computation 2003: 1552-1559 - 2001
- [j1]Anthony J. Bagnall, Victor J. Rayward-Smith, Ian M. Whittley:
The next release problem. Inf. Softw. Technol. 43(14): 883-890 (2001) - 2000
- [c3]