Ludmila Kuncheva's Home Page

Publications

2024

  • Kuncheva L.I., José Luis Garrido-Labrador, Ismael Ramos-Pérez, Samuel L. Hennessey, Juan J. Rodríguez, Semi-Supervised Classification With Pairwise Constraints: A Case Study on Animal Identification from Video, Information Fusion, 104, 2024, 102188, (preprint) (online) [bib]

    • 2023

  • Williams F. J., L. I. Kuncheva, Effects of hyper-parameters in online constrained clustering: A study on animal videos, Proc. of the 4th Symposium on Pattern Recognition and Applications (SPRA), 2023, Naples, Italy, pdf [bib]
  • Kuncheva L.I., José Luis Garrido-Labrador, Ismael Ramos-Pérez, Samuel L. Hennessey, Juan J. Rodríguez, An experiment on animal re-identification from video, Ecological Informatics, 74, 2023, 101994, (preprint) pdf [bib]

    • 2022

  • Kuncheva L. I., F. J. Williams, and S. L. Hennessey, A bibliographic view on constrained clustering, arXiv:2209.11125, 2022, pdf [bib]
  • Kuncheva L. I., F. J. Williams, S. L. Hennessey, and J. J. Rodriguez, A benchmark database for animal re-identification and tracking, Proc. of the Fifth IEEE International Conference on Image Processing, Applications and Systems (IPAS), 2022, pdf [bib]
  • Williams F. J., L. I. Kuncheva, S. L. Hennessey, and J. J. Rodriguez, Combination of object tracking and object detection for animal recognition, Proc. of the Fifth IEEE International Conference on Image Processing, Applications and Systems (IPAS), 2022, pdf [bib]

    • 2021

  • Kuncheva L. I., Animal Reidentification using Restricted Set Classification, Ecological Informatics, 62, 2021, 101225, (preprint) pdf, [bib]

    • 2020

  • Kavur, A. E., L. I. Kuncheva and M. A. Selver, Basic ensembles of vanilla-style deep learning models improve liver segmentation from CT images, arXiv:2001.09647 [eess.IV], 2020, pdf, [bib]
  • Rodríguez J. J., M. Juez-Gil, A. Arnaiz-González and L. I. Kuncheva, An experimental evaluation of Mixup Regression Forests, Expert Systems with Applications, 151, 2020, 113376, (preprint), pdf, [bib]
  • Rodríguez J. J., J. F. Díez-Pastor, A. Arnaiz-González and L. I. Kuncheva, Random Balance ensembles for multiclass imbalance learning, Knowledge-Based Systems, 193, 2020, 105434, (preprint), pdf, [bib]
  • Kuncheva L. I., C. E. Matthews, A. Arnaiz-González and J. J. Rodríguez, Feature Selection from High-Dimensional Data with Very Low Sample Size: A Cautionary Tale, 2020, arXiv, 2020, 2008.12025, pdf , [bib]
  • Kuncheva L. I., Prototype Classifiers and the Big Fish. The Case of Prototype (Instance) Selection, IEEE SMC Magazine, 6(2), 2020, 49-56. pdf [bib]
  • Kuncheva, L. I. and C. C. Gray, A MATLAB Exercise Book, Lulu, 2020, 2nd edition. pdf [bib]

    • 2019

  • Jones, B. D., L. Hardy, G. P. Lawrence, L. I. Kuncheva, R. Brandon, P. Such, and M. Bobat, The Identification of 'Game Changers' in England Cricket's Developmental Pathway for 3 Elite Spin Bowling: A Machine Learning Approach, Journal of Expertise, 2(2), 2019, 92-120. pdf [bib] [url]
  • Güllich A., L. Hardy, L. Kuncheva, S. Laing, M. Barlow, L. Evans, T. Rees, B. Abernethy, J. Côté, C Warr, and L. Wraith, Developmental Biographies of Olympic Super-Elite and Elite Athletes: A Multidisciplinary Pattern Recognition Analysis, Journal of Expertise, 2(1), 2019, 23-46. pdf [bib] [url]
  • Gunn, I. A. D. and L. I. Kuncheva, Bounds for the VC dimension of 1NN prototype sets, arXiv:1902.02660, pdf [bib]
  • Kuncheva L. I., A. Arnaiz-Gonzalez, J. F. Diez-Pastor, and I. A. D. Gunn, Instance selection improves geometric mean accuracy: A study on imbalanced data classification, Progress in Artificial Intelligence, 8(2), 2019, 215-228. arXiv:1804.07155, pdf [bib]
  • Matthews, C. E., L. I. Kuncheva and P. Yousefi, Classification and comparison of on-line video summarisation methods, Machine Vision and Applications, 2019, doi 10.1007/s00138-019-01007-x, pdf [bib] [url]
  • Faithfull, W. J., J. J. Rodriguez-Diez and L. I. Kuncheva, Combining univariate approaches for ensemble change detection in multivariate data, Information Fusion, 45, 2019, 202–214. pdf [bib]
  • Kuncheva, L. I., Pattern Recognition and Neural Networks, Lulu, 2019. pdf [bib]

    • 2018

  • Kuncheva, L. I. and J. H. V. Constance, Restricted Set Classification with prior probabilities: A case study on chessboard recognition, Pattern Recognition Letters, 111, 2018, 36-42. pdf [bib] (data and images)
  • Kuncheva, L. I. and J. J. Rodriguez-Diez, On feature selection protocols for very low-sample-size data, Pattern Recognition, 81, 2018, 660-673. https://doi.org/10.1016/j.patcog.2018.03.012, pdf [bib]
  • Kuncheva, L. I., P. Yousefi and J. Almeida, Edited nearest neighbour for selecting keyframe summaries of egocentric videos, Journal of Visual Communication and Image Representation 52, 2018, 118–130. pdf [bib] (code MATLAB)
  • Gunn, I. A. D., A. Arnaiz-Gonzalez and L. I. Kuncheva, A taxonomic look at instance-based stream classifiers, Neurocomputing, 286, 2018, 167–178. pdf [bib]
  • Zubek J. and L. I. Kuncheva, Learning from exemplars and prototypes in machine learning and psychology, arXiv:1806.01130, 2018. pdf [bib]
  • Yousefi P., C. E. Matthews and L. I. Kuncheva, Budget-constrained online video summarisation of egocentric video using control charts, Proceedings of the International Symposium on Visual Computing (ISVC 2018), Las Vegas, USA, 2018. pdf [bib]
  • Yousefi P., L. I. Kuncheva and C. E. Matthews, Selecting feature representation for online summarisation of egocentric videos, Proceedings of the International Conference on Computer Graphics and Visual Computing (EUROGRAPHICS 2018), Swansea, UK, 2018. pdf (extended version) [bib]
  • Yousefi P. and L. I. Kuncheva, Selective keyframe summarisation for egocentric videos based on semantic concept search, Proceedings of the International Image Processing Applications and Systems Conference (IPAS 2018), Sophia Antipolis, France, 2018. pdf [bib]
  • Matthews C. E., P. Yousefi and L. I. Kuncheva, Using control charts for online video summarisation, Proceedings of the International Joint Conference on Computer Vision and Pattern Recognition (CCVPR 2018), Wellington, New Zealand, 2018. pdf [bib] [url]

    • 2017

  • Kuncheva L. I., P. Yousefi and J. Almeida, Comparing keyframe summaries of egocentric videos: Closest-to-centroid baseline, Proceedings of The Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA 2017), 2017, Montreal, Canada. pdf [bib]
  • Kuncheva L. I., J. J. Rodríguez and A. S. Jackson, Restricted Set Classification: Who is there?, Pattern Recognition, 63, 2017, 158–170. pdf [bib] [code GitHub]
  • Kuncheva L. I., P. Yousefi and I. A. D. Gunn, On the evaluation of video keyframe summaries using user ground truth, arXiv:1712.06899, 2017. pdf [bib] (code MATLAB)
  • Gunn I. A. D., L. I. Kuncheva, and P. Yousefi, Bipartite graph matching for keyframe summary evaluation, arXiv:1712.06914, 2017. pdf [bib] (code MATLAB)

    • 2016

  • Kuncheva L. I., I. A. D. Gunn, A concept-drift perspective on prototype selection and generation, Proceedings of the International Joint Conference on Neural Networks (IJCNN 2016), Vancouver, Canada, 2016, 16-23. pdf [bib]

    • 2015

  • Diez-Pastor J. F., J. J. Rodríguez, C. García-Osorio and L. I. Kuncheva, Diversity techniques improve the performance of the best imbalance learning ensembles, Information Sciences, 325, 2015, 98-117. pdf [bib]
  • Kuncheva L.I. and M. Galar. Theoretical and empirical criteria for the edited nearest neighbour classifier, Proceedings of the 15th IEEE International Conference on Data Mining, Atlantic City, 2015, 817-822. pdf [bib] [code and data]
  • Diez-Pastor J. F., J. J. Rodríguez, C. García-Osorio and L. I. Kuncheva, Random balance: Ensembles of variable priors classifiers for imbalanced data, Knowledge-Based Systems, 85, 2015, 96–111. (DOI http://dx.doi.org/10.1016/j.knosys.2015.04.022). pdf [bib]

    • 2014

  • Kuncheva L.I. Combining Pattern Classifiers. Methods and Algorithms, Wiley, 2nd edition, 2014. pdf [bib] [code GitHub]
  • Christy T. and L.I. Kuncheva, Technological advancements in affective gaming: A historical survey, International Journal on Computing, 3(4), 2014, 32-41. pdf [bib]
  • Kuncheva L.I. and A.S. Jackson, Who is missing? A new pattern recognition puzzle, S+SSPR, 2014, Joensuu, Finland, Springer, LNCS 8621 243–252. pdf [bib]
  • Faithfull W.J. and L.I. Kuncheva, On optimum thresholding of multivariate change detectors, S+SSPR, 2014, Joensuu, Finland Springer, LNCS 8621 364–373. pdf [bib]
  • Kuncheva L.I., D. Martinez-Rego, Kenneth S. L. Yuen, D. E. J. Linden, S. J. Johnston, A spatial discrepancy measure between voxel sets in brain imaging, Signal, Image and Video Processing, 8 (5), 2014, 913-922 (DOI: 10.1007/s11760-012-0326-0). pdf [bib]
  • Kuncheva L. I. and J. J. Rodriguez, A weighted voting framework for classifiers ensembles, Knowledge and Information Systems 38, 2014, 259-275 (DOI: 10.1007/s10115-012-0586-6). pdf [bib]
  • Kuncheva L.I. and W.J. Faithfull, PCA feature extraction for change detection in multidimensional unlabelled data, IEEE Transactions on Neural Networks and Learning Systems, 25(1), 2014, 69-80 (DOI: 10.1109/TNNLS.2013.2248094). pdf [bib]
  • Marín J., D. Vázquez, A. M. López, J. Amores and L. I. Kuncheva, Occlusion handling via random subspace classifiers for human detection, IEEE Transactions on Systems, Man, and Cybernetics (Part B), 2014, 44(3), 342-354 (DOI: 10.1109/TCYB.2013.2255271) pdf [bib]
  • Kuncheva L.I. and C.J. Whitaker, Pattern recognition and classification, Wiley StatsRef-Statistics Reference Online, 2014. pdf [bib]

    • 2013

  • Kuncheva L.I., Change detection in streaming multivariate data using likelihood detectors, IEEE Transactions on Knowledge and Data Engineering, 2013, 25(5), 1175-1180 (DOI: 10.1109/TKDE.2011.226). pdf [bib] [code (GitHub)]
  • Kuncheva L.I., A bound on kappa-error diagrams for analysis of classifier ensembles, IEEE Transactions on Knowledge and Data Engineering, 2013, 25 (3), 494-501 (DOI: 10.1109/TKDE.2011.234). pdf [bib]
  • Christy T. and L.I. Kuncheva, A.M.B.E.R. Shark Fin: An unobtrusive affective mouse, Proc ACHI2013: The 6th International Conference in Computer-Human Interactions, Nice, France, 2013 , 488-495. pdf [bib]
  • Kuncheva L. I. and J. J. Rodriguez, Interval feature extraction for classification of event-related potentials (ERP) in EEG data analysis, Progress in Artificial Intelligence, 2(1), 2013, 65-72 (DOI: 10.1007/s13748-012-0037-3). pdf [bib]

    • 2012

  • Kuncheva L. I., J. J. Rodriguez, Y. I. Syed, C. O. Phillips and K. E. Lewis, Classifier ensemble methods for diagnosing COPD from volatile organic compounds in exhaled air, International Journal of Knowledge Discovery in Bioinformatics 3 (2), 2012, 1-15. pdf [bib]
  • Plumpton C. O., L. I. Kuncheva, N. N. Oosterhof and S. J. Johnston, Naive random subspace ensemble with linear classifiers for real-time classification of fMRI data, Pattern Recognition, 45 (6), 2012, 2101-2108. pdf [bib]
  • Kuncheva, L. I. and W. J. Faithfull, PCA feature extraction for change detection in multidimensional unlabelled streaming data, Proc. 21st International Conference on Pattern Recognition (ICPR 2012), 2012, 1140-1143. pdf [bib]
  • Kuncheva, L.I., C. J. Smith, C, S. Yasir, C. Phillips and K. E.Lewis, Evaluation of feature ranking ensembles for high-dimensional biomedical data: A case study, In Proceedings of the Workshop on Biological Data Mining and its Applications in Healthcare (BioDM), IEEE 12th International Conference on Data Mining, 2012, 49-56. pdf [bib]
  • Christy, T. and L. I. Kuncheva and K. W. Williams, Selection of physiological input modalities for emotion recognition, Technical Report #CS-TR-002-2012, School of Computer Science, Bangor University, UK, 2012. pdf [bib]

    • 2011

  • Kuncheva L.I., T. Christy, I. Pierce and S. P. Mansoor. Multi-modal Biometric Emotion Recognition using Classifier Ensembles, Proc 24th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AEI), NY, Lecture Notes in Computer Science, 2011, LNCS 6703, 317-326. pdf [bib]

    • 2010

  • Kuncheva L.I., Full-Class Set Classification Using the Hungarian Algorithm, International Journal of Machine Learning and Cybernetics, 1 (1), 2010, 53-61. pdf [bib] [code GitHub]
  • Polikar R., J. DePasquale, H. S. Mohammed, G. Brown, L.I. Kuncheva, LEARN++.MF: A Random Subspace Approach for the Missing Feature Problem, Pattern Recognition, 43, 2010, 3817-3832. pdf [bib]
  • Kuncheva L.I., J. J. Rodriguez, Classifier Ensembles for fMRI Data Analysis: An Experiment, Magnetic Resonance Imaging, 28 (4), 2010, 583-593. pdf [bib]
  • Plumpton C. O., L. I. Kuncheva, D. E. J. Linden and S. J. Johnston, On-line fMRI Data Classification Using Linear and Ensemble Classifiers, Proc. ICPR 2010, Istanbul, Turkey, 2010, 4312-4315. pdf [bib]
  • Kuncheva L.I., J. J. Rodriguez, C. O. Plumpton, D. E. J. Linden and S. J. Johnston, Random Subspace Ensembles for fMRI Classification, IEEE Transactions on Medical Imaging, 29 (2), 2010, 531-542. pdf [bib]
  • Kuncheva L.I. and C. O. Plumpton, Choosing parameters for Random Subspace ensembles for fMRI classification, Proc. Multiple Classifier Systems (MCS'10), Cairo, Egypt, LNCS 5997, 2010, 54-63. pdf [bib]
  • Brown G., L.I. Kuncheva, "Good" and "bad" diversity in majority vote ensembles, Proc. Multiple Classifier Systems (MCS'10), Cairo, Egypt, LNCS 5997, 2010, 124-133. pdf [bib]
  • Dainotti A., F. Gargiulo, L.I. Kuncheva, A. Pescape and C. Sansone, Identification of traffic flows hiding behind TCP port 80, Proc. IEEE International Conference on Communications (ICC 2010), 2010, Cape Town, South Africa. pdf [bib]
  • Zliobaite I. and L.I. Kuncheva, Theoretical window size for classification in the presence of sudden concept drift, Technical Report # BCS-TR-001-2010, Bangor University, UK, 2010. pdf [bib]

    • 2009

  • Zliobaite I. and L. I. Kuncheva, Determining the Training Window for Small Sample Size Classification with Concept Drift, Proc. 1st International Workshop on Transfer Mining (TM'09), In conjunction with the 2009 IEEE International Conference on Data Mining (ICDM 2009), Dec 6-9, 2009, Miami, Florida, USA. pdf [bib]
  • Gargiulo F., L. I. Kuncheva and C. Sansone. Network Protocol Verification by a Classifier Selection Ensemble, Proc. MCS 2009, Reykjavik, Iceland, Lecture Notes in Computer Science Volume 5519, 314-323. pdf [bib]
  • Kuncheva L.I. and I. Zliobaite, On the Window Size for Classification in Changing Environments, Intelligent Data Analysis, 13 (6), 2009, 861-872. pdf, some simulation code (Fig 4.) [bib]
  • Charles J.J., L.I. Kuncheva, B. Wells and I.S. Lim, Stability of kerogen classification with regard to image segmentation, Mathematical Geology, 41, 2009, 475-486. pdf [bib]
  • Kuncheva L.I., Using Control Charts for Detecting Concept Change in Streaming Data, Technical Report, BCS-TR-001-2009, School of Computer Science, Bangor University, UK, 2009. pdf [bib]

    • 2008

  • Kuncheva L.I. Classifier Ensembles: Facts, Fiction, Faults and Future, 19th International Conference on Pattern Recognition (ICPR), Tampa, Florida, 2008, (plenary talk, not reviewed). slides (ppt) [bib]
  • Kuncheva L.I. and C.O. Plumpton, Adaptive learning rate for online linear discriminant classifiers, Proc Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition S+SSPR, Orlando, Florida, USA , 2008, 510-519. pdf [bib]
  • Rodriguez J.J and L.I. Kuncheva, Combining online classification approaches for changing environments, Proc Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition S+SSPR, Orlando, Florida, USA , 2008, 520-529. pdf [bib]
  • Kuncheva L.I. and J. S. Sanchez, Nearest neighbour classifiers for streaming data with delayed labelling, Proc. IEEE International Conference on Data Mining (ICDM08), Pisa, Italy, 2008, 869-874. pdf [bib]
  • Kuncheva L.I. Classifier ensembles for detecting concept change in streaming data: Overview and perspectives, Proc. 2nd Workshop SUEMA 2008 (ECAI 2008), Patras, Greece, 2008, 5-10. pdf
  • Kuncheva L.I., C.J. Whitaker and A. Narasimhamurthy, A case study on naive labelling for the nearest mean and the linear discriminant classifiers, Pattern Recognition, 41, 2008, 3010-3020. pdf
  • Kuncheva L.I., J.J. Charles, N. Miles, A. Collins, B. Wells and I.S. Lim, Automated kerogen classification in microscope images of dispersed kerogen preparation, Mathematical Geosciences, 40, 2008, 639-652. pdf
  • Kuncheva L.I. and Z.S.J. Hoare, Error-dependency relationships for the Naive Bayes classifier with binary features, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30 (4), 2008, 735-740. pdf
  • Charles J.J., L.I. Kuncheva, B. Wells and I.S. Lim, Background segmentation in microscope images, Proc 3rd International Conference on Computer Vision Theory and Applications VISAPP08, Madeira, Portugal, 2008. pdf
  • Charles J.J, L.I. Kuncheva, B. Wells and I.S. Lim, Object segmentation within microscope images of palynofacies, Computers & Geosciences, 34, 2008, 688-698. pdf
  • Kuncheva L.I. Fuzzy classifiers, Scholarpedia, 3(1):2925. article

    • 2007

  • Rodriguez J.J and L.I. Kuncheva, Time series classification: Decision forests and SVM on interval and DTW features, Proc Workshop no Time Series Classification, 13th International Conference on Knowledge Discovery and Data mining, San Jose, CA, 2007. pdf
    • *** Winner of the 13th KDD Challenge on Time Series Classification
  • Kuncheva L.I. and J.J. Rodriguez, Classifier ensembles with a random linear oracle, IEEE Transactions on Knowledge and Data Engineering, 19 (4), 2007, 500-508. pdf
  • Kuncheva L.I., V. del Rio Villas and J.J. Rodriguez, Diagnosing scrapie in sheep: A classification experiment, Computers in Biology and Medicine,37 (8), 2007, 1194-1202.
  • Kuncheva L.I and J.J. Rodriguez, An experimental study on Rotation Forest ensembles, Proc 7th International Workshop on Multiple Classifier Systems, MCS'07, Prague, Czech Republic, 2007, LNCS 4472, 459-468. pdf
  • Rodriguez J.J and L. I. Kuncheva, Naive Bayes ensembles with a random oracle, Proc 7th International Workshop on Multiple Classifier Systems, MCS'07, Prague, Czech Republic, 2007, LNCS 4472, 450-458. pdf
  • Hadjitodorov S. T. and L. I. Kuncheva, Selecting diversifying heuristics for cluster ensembles, Proc 7th International Workshop on Multiple Classifier Systems, MCS'07, Prague, Czech Republic, 2007, LNCS 4472, 200-209. pdf
  • Sanchez J. S. and L.I. Kuncheva, Data reduction using classifier ensembles, Proc. 11th European Symposium on Artificial Neural Networks, Bruges, Belgium, 2007. pdf
  • Kuncheva L.I, A stability index for feature selection, Proc. IASTED, Artificial Intelligence and Applications, Innsbruck, Austria, 2007, 390-395. pdf [bib]
  • Narasimhamurthy A., L.I. Kuncheva, A framework for generating data to simulate changing environments, Proc. IASTED, Artificial Intelligence and Applications, Innsbruck, Austria, 2007, 384-389. pdf [bib]
    • [code] [code (GitHub)]

    2006

  • Charles J.J, L.I. Kuncheva, B. Wells, I.S. Lim, An evaluation measure of image segmentation based on object centres, Proc. International Conference on Image Analysis and Recognition ICIAR, 2006, Portugal, LNCS 4141, 283-294. pdf
  • Kuncheva L.I., D.P. Vetrov, Evaluation of stability of k-means cluster ensembles with respect to random initialization, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (11), 2006, 1798-1808. pdf
  • Kuncheva L.I., S.T. Hadjitodorov, L.P. Todorova, Experimental comparison of cluster ensemble methods, Proc FUSION 2006, Florence, Italy, 2006. pdf
  • Rodriguez J.J, L.I. Kuncheva, C.J. Alonso, Rotation Forest: A new classifier ensemble method, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (10), 2006, 1619-1630. pdf [bib]
  • Kuncheva L.I., On the optimality of Naive Bayes with dependent binary features, Pattern Recognition Letters, 27, 2006, 830-837. pdf
  • Hadjitodorov S.T., L. I. Kuncheva, L. P. Todorova, Moderate diversity for better cluster ensembles, Information Fusion, 7 (3), 2006, 264-275. pdf
  • Vilarino F., L.I. Kuncheva, P. Radeva, ROC curves and video analysis optimization in intestinal capsule endoscopy, Pattern Recognition Letters, 27, 2006, 875-881. pdf
  • Kuncheva L.I., S.T. Hadjitodorov, Diversifying Heuristics for Cluster Ensembles, Technical Report, December 2006. pdf

    • 2005

  • Masip D., L.I. Kuncheva, J. Vitria, An ensemble-based method for linear feature extraction for two-class problems, Pattern Analysis and Applications, 8, 2005, 227-237. pdf
  • Kuncheva L.I., C.J. Whitaker, Pattern Recognition, Encyclopedia of Statistics in Behavioral Science, Wiley, Chichester, 2005, 3, 1532-1535. pdf
  • Kuncheva L.I., Z.S.J. Hoare, P.D. Cockcroft, Selection of independent binary features using probabilities: An example from veterinary medicine, Journal of Modern Applied Statistical Methods, 4 (2), 2005, 528-537. pdf
  • Kuncheva L.I., Using diversity measures for generating error-correcting output codes in classifier ensembles, Pattern Recognition Letters, 26, 2005, 83-90. pdf
  • Kuncheva L.I. Diversity in multiple classifier systems (Editorial), Information Fusion, 6 (1), 2005, 3-4. pdf
    • (Special issue on Diversity in Multiple Classifier Systems)

    2004

  • Kuncheva L.I. Combining Pattern Classifiers. Methods and Algorithms, Wiley, 2004.
  • Kuncheva L.I., S.T. Hadjitodorov, Using diversity in cluster ensembles, Proc. IEEE International Conference on Systems, Man and Cybernetics, The Hague, The Netherlands, 2004, 1214-1219. pdf
  • Kuncheva L.I., Classifier ensembles for changing environments, Proceedings 5th International Workshop on Multiple Classifier Systems, MCS2004, Cagliari, Italy, in F. Roli, J. Kittler and T. Windeatt (Eds.), Lecture Notes in Computer Science, Vol 3077, 2004, 1-15. pdf [bib]
  • Whitaker C.J., L.I. Kuncheva, P.D. Cockcroft, A logodds criterion for selection of diagnostic tests, Proc IAPR International Workshop on Statistical Pattern Recognition, Lisbon, Portugal, 2004, 575-582. pdf
  • Kuncheva L.I., P.D. Cockcroft, C.J. Whitaker, Z.S. Hoare, Pre-selection of independent binary features: An application to diagnosing scrapie in sheep, Proceedings of 20th Conference on Uncertainty in Artificial Intelligence, Banff, Canada, 2004, 325-332. pdf

    • 2003

  • Kuncheva L.I. Fuzzy vs Non-fuzzy in combining classifiers designed by boosting, IEEE Transactions on Fuzzy Systems, 11 (6), 2003, 729-741. pdf
    • *** Won the best paper award for 2006 in IEEE Transactions on Fuzzy Systems.
  • Kuncheva L.I., C.J. Whitaker, C.A. Shipp, R.P.W. Duin. Limits on the majority vote accuracy in classifier fusion, Pattern Analysis and Applications, 6, 2003, 22-31. pdf
  • Kuncheva L.I., C.J. Whitaker. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy, Machine Learning, 51, 2003, 181-207. pdf[bib]
  • Kuncheva L.I.. That elusive diversity in classifier ensembles, Proc IbPRIA 2003, Mallorca, Spain, 2003, Lecture Notes in Computer Science, Springer-Verlag, LNCS 2652, 1126-1138. pdf
  • Kuncheva L.I. Error bounds for aggressive and conservative AdaBoost, Proc MCS 2003, Guilford, UK, Lecture Notes in Computer Science, Springer-Verlag, LNCS 2709, 25-34. pdf
  • Whitaker C.J., L.I. Kuncheva, Examining the relationship between majority vote accuracy and diversity in bagging and boosting, Technical Report, 2003, School of Informatics, University of Wales, Bangor. pdf
  • Grant A., D. Last, L. Kuncheva, N. Ward, Marine DGNSS availability and continuity, The Journal of Navigation, 56, 2003, 353-369. pdf

    • 2002

  • Kuncheva L.I., M. Skurichina, R.P.W. Duin. An experimental study on diversity for bagging and boosting with linear classifiers, Information Fusion, 3 (2), 2002, 245-258. pdf
  • Shipp C.A. and L.I. Kuncheva. Relationships between combination methods and measures of diversity in combining classifiers, Information Fusion, 3 (2), 2002, 135-148. pdf
  • Kuncheva L.I. Switching between selection and fusion in combining classifiers: An experiment, IEEE Transactions on SMC, Part B, 32 (2), 2002, 146-156. pdf
    • *** Won the Sage best Transaction paper award for 2003 across IEEE Transactions on SMC A, B and C.
  • Kuncheva L.I., R.K. Kountchev. Generating classifier outputs of fixed accuracy and diversity, Pattern Recognition Letters, 23, 2002, 593-600. pdf
  • Kuncheva L.I. A theoretical study on six classifier fusion strategies, IEEE Transactions on PAMI, 24, (2), 2002, 281-286. pdf
  • Shipp C.A. and L.I. Kuncheva. An investigation into how AdaBoost affects classifier diversity, Proc. IPMU 2002, Annecy, France, 2002, 203-208. pdf
  • Kuncheva L.I. and C.J. Whitaker. Using diversity with three variants of boosting: aggressive, conservative and inverse, Proc. MCS 2002, Cagliari, Italy, Lecture Notes in Computer Science, Springer-Verlag, 2364, 81-90. pdf
  • Skurichina M., L.I. Kuncheva and R.P.W. Duin. Bagging and Boosting for the nearest mean classifier: Effects of sample size on diversity and accuracy, Proc. MCS 2002, Cagliari, Italy, Lecture Notes in Computer Science, Springer-Verlag, 2364, 62-71.

    • 2001

  • Kuncheva L.I., J. Wrench, L.C. Jain, and A. Al-Zaidan. A fuzzy model of heavy metal loadings in marine environment, in: Da Ruan, J. Kacprzyk and M. Fedrizzi (Eds.) Soft Computing for Risk Assessment and Management, Springer-Verlag group (Physica-Verlag, Heidelberg and New York), series Studies in Fuzziness and Soft Computing, 2001, 355-371. pdf
  • Kuncheva L.I. Combining classifiers: Soft computing solutions, in: S.K. Pal and A. Pal (Eds.) Pattern Recognition: From Classical to Modern Approaches, World Scientific Publishing Co., Singapore, 2001, 427-452. pdf
  • Bezdek J.C., L.I. Kuncheva. Nearest prototype classifier designs: An experimental study, International Journal of Intelligent Systems, 16 (12), 2001, 1445-1473. pdf
  • Kuncheva L.I. Using measures of similarity and inclusion for multiple classifier fusion by decision templates, Fuzzy Sets and Systems, 122, (3), 2001, 401-407. pdf
  • Kuncheva L.I., J.C. Bezdek and R.P.W. Duin. Decision templates for multiple classifier fusion, Pattern Recognition, 34 (2), 2001, 299-314. pdf
  • Kuncheva L.I. Fuzzy vs Non-fuzzy in combining classifiers: an experimental study, Proc LFA'01, Mons, Belgium, 2001, 11-22.
  • Kuncheva L.I., F. Roli, G.L. Marcialis, C.A. Shipp, Complexity of data subsets generated by the random subspace method: An experimental investigation, MCS 2001, Cambridge, Lecture Notes in Computer Science, LNCS 2096, 349-358. pdf
  • Kuncheva L.I., C.J. Whitaker, Feature subsets for classifier combination: An enumerative experiment, MCS 2001, Cambridge, Lecture Notes in Computer Science, LNCS 2096 228-237. pdf
  • Shipp C.A., L.I. Kuncheva, Four measures of data complexity for bootstrapping, splitting and feature sampling, Proc. CIMA 2001, Bangor, June, 2001, 429-435. pdf
  • Al-Zaidan A.S., L.I. Kuncheva, Using fuzzy similarities to analyze heavy metal distribution in a marine environment, Proc. CIMA 2001, Bangor, June, 2001, 725-731. pdf
  • Kuncheva L.I., C.J. Whitaker, Ten measures of diversity in classifier ensembles: Limits for two classifiers, IEE Workshop on Intelligent Sensor Processing, Birmingham, February, 2001, 10/1-10/6. pdf

    • 2000

  • Kuncheva L.I, Fuzzy Classifier Design, Springer-Verlag, Heidelberg, May 2000.
  • Kuncheva L.I., How good are fuzzy if-then classifiers? IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 30 (4), 2000, 501-509. pdf
  • Kuncheva L.I. and L.C. Jain, Designing classifier fusion systems by genetic algorithms, IEEE Transactions on Evolutionary Computation, 4 (4), 2000, 327-336. pdf
  • Kuncheva L.I., J. Wrench, L.C. Jain and A.S. Al-Zaidan, A fuzzy model of heavy metal loadings in Liverpool bay, Environmental Modelling and Software, 15 (2), 2000, 161-167. pdf
  • Bezdek, J. C. and L.I. Kuncheva, Some notes on 21 nearest prototype classifiers, Advances in Pattern Recognition, Lecture Notes in Computer Science, 1876, eds. F. J. Ferri, J. M. Inesta, A. Amin and P. Pudil, Springer, Berlin, 2000, 1-16. pdf
  • Kuncheva L.I., C.J. Whitaker, C.A. Shipp and R.P.W. Duin. Is independence good for combining classifiers?, Proc. 15th International Conference on Pattern Recognition, Barcelona, Spain, 2000, 2, 168-171. pdf
  • Kuncheva L.I. Cluster-and-selection method for classifier combination, Proc. 4th International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies (KES'2000), Brighton, UK, 2000, 185-188. pdf
  • Al-Zaidan, A.S., and L.I. Kuncheva. Selecting fuzzy connectives to represent heavy metal distribution in Liverpool Bay, Proc. 4th International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies (KES'2000), Brighton, UK, 2000, 602-605. pdf

    • 1999

  • Kuncheva L.I. and L.C. Jain, Nearest neighbor classifier: Simultaneous editing and feature selection, Pattern Recognition Letters, 20, 1999, 1149-1156. pdf
  • Bezdek J.C., J.M. Keller, R. Krishnapuram and L.I. Kuncheva, N.R. Pal, Will the real Iris data please stand up?, IEEE Transactions on Fuzzy Systems, 7(3), 1999, 368-369. pdf
  • Kuncheva L.I. and J.C. Bezdek, Presupervised and postsupervised prototype classifier design, IEEE Transactions on Neural Networks, 10 (5), 1999, 1142-1152. pdf
  • Kuncheva L.I. and F. Steimann, Fuzzy Diagnosis (Editorial), Artificial Intelligence in Medicine, 16(2), 1999, 121-128. pdf
    • (Special issue on Fuzzy Diagnosis)

    ...1998

  • Kuncheva L.I. and J.C. Bezdek, An integrated framework for generalized nearest prototype classifier design, International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, 6 (5), 1998, 437-457. pdf
  • Kuncheva L.I., J.C. Bezdek. Nearest prototype classification: Clustering, genetic algorithms or random search? IEEE Transactions on Systems, Man, and Cybernetics, C28 (1), 1998, 160-164. pdf
  • Kuncheva L.I., J.C. Bezdek and M.A. Sutton, On combining multiple classifiers by fuzzy templates, Proc NAFIPS'98, Pensacola, Florida, 1998, 193-197. pdf
  • Kuncheva L.I. Fitness functions in editing k-NN reference set by genetic algorithms, Pattern Recognition, 30, 1997, 1041-1049. pdf
  • Kuncheva L.I. An Application of OWA Operators to the Aggregation of Multiple Classification Decisions, in: Yager, R. & Kacprzyk, J. (Eds.) The Ordered Weighted Averaging Operators, Springer US, 1997, 330-343. pdf [bib]
  • Kuncheva L.I. Initializing an RBF neural network by a genetic algorithm, Neurocomputing, 14, 1997, 273-288. pdf
  • Kuncheva L.I. On the equivalence between fuzzy and statistical classifiers, International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, 4 (3), 1996, 245-253. pdf
  • Kuncheva L. Editing for the k-nearest neighbors rule by a genetic algorithm, Pattern Recognition Letters, Special Issue on Genetic Algorithms, 16, 1995, 809-814. pdf
  • Mitra S., L. Kuncheva. Improving classification performance using fuzzy MLP and two-level selective partitioning of the feature space, Fuzzy Sets and Systems, 70, 1995, 1-13. pdf
  • Kuncheva L.I. On combining multiple classifiers, Proc. 7th International Conference on Information Processing and Management of Uncertainty (IPMU'98), Paris, France, 1998, 1890-1891. pdf
  • Kuncheva L.I., J.C. Bezdek. Selection of cluster prototypes from data by a genetic algorithm, Proc. 5th European Congress on Intelligent Techniques and Soft Computing, EUFIT, Aachen, Germany, 1997, 1683-1688. pdf
  • Kuncheva L.I., R. Krishnapuram. A fuzzy consensus aggregation operator, Fuzzy Sets and Systems, 79, 1996, 347-356. pdf
  • Kuncheva L. I., K. T. Atanassov. An intuitionistic fuzzy RBF network, Proc. 4th European Congress on Intelligent Techniques and Soft Computing, EUFIT, Aachen, Germany, 1996, 777-781. pdf [bib]
  • Kuncheva L.I. Using degree of consensus in two-level fuzzy pattern recognition, European Journal of Operational Research, 80, 1995, 365-370. pdf
  • Kuncheva L.I., S. Mitra. A two-level classification scheme trained by a fuzzy neural network, Proc. 12 International Conference on Pattern Recognition, Jerusalem, Israel, 1994, 467-469. pdf
  • Mitra S., L.I. Kuncheva, Change-glasses pattern classification with a fuzzy neural network, Proc 2nd International Conference on FUzzy Based Expert SysTems, FUBEST, Sofia, Bulgaria, 1994, 29-32. pdf
  • Kuncheva, L. I. Genetic algorithm for feature selection for parallel classifiers, Information Processing Letters, 46, 1993, 163-168. pdf
  • Kuncheva, L. I. Fuzzy rough sets: application to feature selection, Fuzzy Sets and Systems, 51, 1992, 147-153. pdf