# Prof Ludmila Kuncheva

Professor in Computer Science

School of Computer Science, Bangor University Dean Street, Bangor, Gwynedd LL57 1UT

e-mail: l.i.kuncheva@bangor.ac.uk

### Teaching and Supervision

ICP1016 Mathematics for Computing

ICP2021 Algorithm Design with MATLAB

ICP3083 Pattern Recognition and Neural Networks

ICP3099 Final Year Project

### Research Interests

Pattern Recognition, Machine Learning

### Publications

#### 2017

- Restricted Set Classification: Who is there?, Pattern RecognitionKuncheva, L, Rodriguez, J & Jackson, A 2017, 'Restricted Set Classification: Who is there?, Pattern Recognition'
*Pattern Recognition*, vol 63, pp. 158-170.

#### 2016

- A Concept-Drift Perspective on Prototype Selection and GenerationKuncheva, L & Gunn, I 2016, A Concept-Drift Perspective on Prototype Selection and Generation. in
*Proceedings of the International Joint Conference on Neural Networks (IJCNN 2016) .*Vancouver, Canada, pp. 16-23, International Joint Conference on Neural Networks, Vancouver, Canada, 24-29 July.

#### 2015

- Diversity techniques improve the performance of the best imbalance learning ensemblesDiez-Pastor, JF, Rodriguez, JJ, Garcia-Osorio, CI & Kuncheva, LI 2015, 'Diversity techniques improve the performance of the best imbalance learning ensembles'
*Information Sciences*, vol 325, pp. 98-117. DOI: 10.1016/j.ins.2015.07.025 - Random Balance: Ensembles of variable priors classifiers for imbalanced dataDiez-Pastor, JF, Rodriguez, JJ, Garcia-Osorio, C & Kuncheva, LI 2015, 'Random Balance: Ensembles of variable priors classifiers for imbalanced data'
*Knowledge-Based Systems*, vol 85, pp. 96-111. DOI: 10.1016/j.knosys.2015.04.022

#### 2014

- On Optimum Thresholding of Multivariate Change DetectorsFaithfull, WJ, Kuncheva, LI, Franti, P (ed.), Brown, G (ed.), Loog, M (ed.), Escolano, F (ed.) & Pelillo, M (ed.) 2014, On Optimum Thresholding of Multivariate Change Detectors. in
*Structural, Syntactic, and Statistical Pattern Recognition, Lecture Notes in Computer Science.*2014 edn, Springer Berlin Heidelberg, pp. 364-373. DOI: 10.1007/978-3-662-44415-3_37 - Who Is Missing? A New Pattern Recognition PuzzleKuncheva, LI, Kuncheav, LI, Jackson, AS, Franti, P (ed.), Brown, G (ed.), Loog, M (ed.), Escolano, F (ed.) & Pelillo, M (ed.) 2014, Who Is Missing? A New Pattern Recognition Puzzle. in
*Structural, Syntactic, and Statistical Pattern Recognition, Lecture Notes in Computer Science.*2014 edn, Springer Berlin Heidelberg, pp. 243-252. DOI: 10.1007/978-3-662-44415-3_25 - Technological Advancements in Affective Gaming: A Historical SurveyChristy, TP, Christy, T & Kuncheva, LI 2014, 'Technological Advancements in Affective Gaming: A Historical Survey'
*GSTF Journal on Computing*, vol 3, no. 4, pp. 38. DOI: 10.7603/s40601-013-0038-5 - A spatial discrepancy measure between voxel sets in brain imagingKuncheva, LI, Martinez-Rego, D, Yuen, KS, Linden, DE & Johnston, SJ 2014, 'A spatial discrepancy measure between voxel sets in brain imaging'
*Signal, Image and Video Processing*, vol 8, no. 5, pp. 913-922. DOI: 10.1007/s11760-012-0326-0 - PCA Feature Extraction for Change Detection in Multidimensional Unlabeled DataKuncheva, LI & Faithfull, WJ 2014, 'PCA Feature Extraction for Change Detection in Multidimensional Unlabeled Data'
*IEEE Transactions on Neural Networks and Learning Systems*, vol 25, no. 1, pp. 69-80. DOI: 10.1109/TNNLS.2013.2248094 - A weighted voting framework for classifiers ensemblesKuncheva, LI & Rodríguez, JJ 2014, 'A weighted voting framework for classifiers ensembles'
*Knowledge and Information Systems*, vol 38, no. 2, pp. 259-275. DOI: 10.1007/s10115-012-0586-6

#### 2013

- Occlusion Handling via Random Subspace Classifiers for Human DetectionKuncheva, LI, Marin, J, Vazquez, D, Lopez, AM, Amores, J & Kuncheva L.I., NV 2013, 'Occlusion Handling via Random Subspace Classifiers for Human Detection'
*IEEE Transactions on Cybernetics*, vol 44, no. 3, pp. 342-354. DOI: 10.1109/TCYB.2013.2255271 - Change detection in streaming multivariate data using likelihood detectorsKuncheva, LI 2013, 'Change detection in streaming multivariate data using likelihood detectors'
*IEEE Transactions on Knowledge and Data Engineering*, vol 25, no. 5, pp. 1175-1180. DOI: 10.1109/TKDE.2011.226 - A Bound on Kappa-Error Diagrams for Analysis of Classifier EnsemblesKuncheva, LI 2013, 'A Bound on Kappa-Error Diagrams for Analysis of Classifier Ensembles'
*IEEE Transactions on Knowledge and Data Engineering*, vol 25, no. 3, pp. 494-501. DOI: 10.1109/TKDE.2011.234 - A.M.B.E.R. Shark-Fin: An Unobtrusive Affective MouseChristy, TP, Christy, T & Kuncheva, LI 2013, 'A.M.B.E.R. Shark-Fin: An Unobtrusive Affective Mouse' Paper presented at ACHI2013: The 6th International Conference in Computer-Human Interactions, Nice, France, 3/01/01, pp. 488-495.

#### 2012

- Interval feature extraction for classification of event-related potentials (ERP) in EEG data analysisKuncheva, LI & Rodriguez, JJ 2012, 'Interval feature extraction for classification of event-related potentials (ERP) in EEG data analysis'
*Progress in Artificial Intelligence*, vol 2, no. 1, pp. 65-72. DOI: 10.1007/s13748-012-0037-3 - Naive random subspace ensemble with linear classifiers for real-time classification of fMRI dataPlumpton, CO, Kuncheva, LI, Oosterhof, NN & Johnston, SJ 2012, 'Naive random subspace ensemble with linear classifiers for real-time classification of fMRI data'
*Pattern Recognition*, vol 45, no. 6, pp. 2101-2108. DOI: 10.1016/j.patcog.2011.04.023

#### 2010

- Full-class set classification using the Hungarian algorithm.Kuncheva, LI 2010, 'Full-class set classification using the Hungarian algorithm.'
*International Journal of Machine Learning and Cybernetics*, vol 1, no. 1-4, pp. 53-61. DOI: 10.1007/s13042-010-0002-z - Learn ++.MF: A random subspace approach for the missing feature problem.Polikar, R, DePasquale, J, Mohammed, HS, Brown, G & Kuncheva, LI 2010, 'Learn ++.MF: A random subspace approach for the missing feature problem.'
*Pattern Recognition*, vol 43, no. 11, pp. 3817-3832. DOI: 10.1016/j.patcog.2010.05.028 - Classifier ensembles for fMRI data analysis: an experiment.Kuncheva, LI & Rodriguez, JJ 2010, 'Classifier ensembles for fMRI data analysis: an experiment.'
*Magnetic Resonance Imaging*, vol 28, no. 4, pp. 583-593. DOI: 10.1016/j.mri.2009.12.021 - Random Subspace Ensembles for fMRI Classification.Kuncheva, LI, Rodriguez, JJ, Plumpton, CO, Linden, DE & Johnston, SJ 2010, 'Random Subspace Ensembles for fMRI Classification.'
*IEEE Transactions on Medical Imaging*, vol 29, no. 2, pp. 531-542. DOI: 10.1109/tmi.2009.2037756

#### 2009

- On the window size for classification in changing environments.Kuncheva, LI & Žliobaitė, I 2009, 'On the window size for classification in changing environments.'
*Intelligent Data Analysis*, vol 13, no. 6, pp. 861-872. DOI: 10.3233/ida-2009-0397 - Stability of Kerogen Classification with Regard to Image SegmentationCharles, JJ, Kuncheva, LI, Wells, B & Lim, IS 2009, 'Stability of Kerogen Classification with Regard to Image Segmentation'
*Mathematical Geosciences*, vol 41, no. 4, pp. 475-486. DOI: 10.1007/s11004-009-9219-3

#### 2008

- Adaptive Learning Rate for Online Linear Discriminant ClassifiersKuncheva, LI & Plumpton, CO 2008, Adaptive Learning Rate for Online Linear Discriminant Classifiers. in
*Proceedings of the IAPR Workshop on Statistical, Structural and Syntactic Pattern Recognition (S+SSPR 2008).*Orlando, Florida, USA, pp. 510-519, IAPR Workshop on Statistical, Structural and Syntactic Pattern Recognition, Orlando, Florida, United States, 4-6 December. - A case-study on naive labelling for the nearest mean and the linear discriminant classifiersKuncheva, LI, Whitaker, CJ & Narasimhamurthy, A 2008, 'A case-study on naive labelling for the nearest mean and the linear discriminant classifiers'
*Pattern Recognition*, vol 41, no. 10, pp. 3010-3020. DOI: 10.1016/j.patcog.2008.03.028 - Automated kerogen classification in microscope images of dispersed kerogen preparationKuncheva, LI, Charles, JJ, Miles, N, Collins, A, Wells, B & Lim, IS 2008, 'Automated kerogen classification in microscope images of dispersed kerogen preparation'
*Mathematical Geosciences*, vol 40, no. 6, pp. 639-652. DOI: 10.1007/s11004-008-9163-7 - Object segmentation within microscope images of palynofacies.Charles, JJ, Kuncheva, LI, Wells, B & Lim, IS 2008, 'Object segmentation within microscope images of palynofacies.'
*Computers and Geosciences*, vol 34, no. 6, pp. 688-698. DOI: 10.1016/j.cageo.2007.09.014 - Error-dependency relationships for the Naive Bayes classifier with binary featuresKuncheva, L & Hoare, ZS 2008, 'Error-dependency relationships for the Naive Bayes classifier with binary features'
*IEEE Transactions on Pattern Analysis and Machine Intelligence*, vol 30, no. 4, pp. 735-740. DOI: 10.1109/TPAMI.2007.70845, 10.1109/TPAMI.2007.70845 - Background segmentation in microscope images.Charles, JJ, Kuncheva, LI, Wells, B & Lim, IS 2008, 'Background segmentation in microscope images.' Paper presented at Proceedings of the 3rd International Conference on Computer Vision Theory and Applications VISAPP08, 3/01/01, .
- Classifier ensembles for detecting concept change in streaming data: Overview and perspectivesKuncheva, LI 2008, 'Classifier ensembles for detecting concept change in streaming data: Overview and perspectives' Paper presented at Proceedings of the 2nd Workshop SUEMA 2008 (ECAI 2008), 3/01/01, pp. 5-10.

#### 2007

- Diagnosing scrapie in sheep: A classification experiment.Kuncheva, LI, Vilas, VJ & Rodriguez, JJ 2007, 'Diagnosing scrapie in sheep: A classification experiment.'
*Computers in Biology and Medicine*, vol 37, no. 8, pp. 1194-1202. DOI: 10.1016/j.compbiomed.2006.10.011 - Classifier ensembles with a random linear oracle.Kuncheva, LI & Rodriguez, JJ 2007, 'Classifier ensembles with a random linear oracle.'
*IEEE Transactions on Knowledge and Data Engineering*, vol 19, no. 4, pp. 500-508. DOI: 10.1109/TDKE.2007.1016 - A framework for generating data to simulate changing environments.Narasimhamurthy, A & Kuncheva, LI 2007, 'A framework for generating data to simulate changing environments.' Paper presented at Proceedings of IASTED, Artificial Intelligence and Applications, 3/01/01, pp. 384-389.
- A stability index for feature selection.Kuncheva, LI 2007, 'A stability index for feature selection.' Paper presented at Proceedings of IASTED, Artificial Intelligence and Applications, 3/01/01, pp. 390-395.
- An experimental study on Rotation Forest ensembles.Kuncheva, LI & Rodriguez, JJ 2007, 'An experimental study on Rotation Forest ensembles.' Paper presented at Proceedings of the 7th International Workshop on Multiple Classifier Systems, 3/01/01, pp. 459-468.
- Data reduction using classifier ensembles.Sanchez, JS & Kuncheva, LI 2007, 'Data reduction using classifier ensembles.' Paper presented at Proceedings of the 11th European Symposium on Artificial Neural Networks, 3/01/01, .
- Naive Bayes ensembles with a random oracle.Rodriguez, JJ & Kuncheva, LI 2007, 'Naive Bayes ensembles with a random oracle.' Paper presented at Proceedings of the 7th International Workshop on Multiple Classifier Systems, 3/01/01, pp. 450-458.
- Selecting diversifying heuristics for cluster ensembles.Hadjitodorov, ST & Kuncheva, LI 2007, 'Selecting diversifying heuristics for cluster ensembles.' Paper presented at Proceedings of the 7th International Workshop on Multiple Classifier Systems, MCS’07, 3/01/01, pp. 200-209.
- Time series classification: Decision forests and SVM on interval and DTW featuresRodriguez, JJ & Kuncheva, LI 2007, 'Time series classification: Decision forests and SVM on interval and DTW features' Paper presented at Proceedings of the Workshop on Time Series Classification, 13th International Conference on Knowledge Discovery and Data mining, 3/01/01, .

#### 2006

- Evaluation of stability of k-means cluster ensembles with respect to random initializationKuncheva, LI & Vetrov, DP 2006, 'Evaluation of stability of k-means cluster ensembles with respect to random initialization'
*IEEE Transactions on Pattern Analysis and Machine Intelligence*, vol 28, no. 11, pp. 1798-1808. - Rotation forest: A new classifier ensemble method.Rodriguez, JJ & Kuncheva, LI 2006, 'Rotation forest: A new classifier ensemble method.'
*IEEE Transactions on Pattern Analysis and Machine Intelligence*, vol 28, no. 10, pp. 1619-1630. - Moderate diversity for better cluster ensembles.Hadjitodorov, ST, Kuncheva, LI & Todorova, LP 2006, 'Moderate diversity for better cluster ensembles.'
*Information Fusion*, vol 7, no. 3, pp. 264-275. DOI: 10.1016/j.inffus.2005.01.008 - ROC curves and video analysis optimization in intestinal capsule endoscopy.Vilarino, F, Kuncheva, LI & Radeva, P 2006, 'ROC curves and video analysis optimization in intestinal capsule endoscopy.'
*Pattern Recognition Letters*, vol 27, no. 8, pp. 875-881. DOI: 10.1016/j.patrec.2005.10.011 - On the optimality of Naive Bayes with dependent binary features.Kuncheva, LI 2006, 'On the optimality of Naive Bayes with dependent binary features.'
*Pattern Recognition Letters*, vol 27, no. 7, pp. 830-837. DOI: 10.1016/j.patrec.2005.12.001 - An evaluation measure of image segmentation based on object centres.Charles, JJ, Kuncheva, LI, Wells, B & Lim, IS 2006, 'An evaluation measure of image segmentation based on object centres.' Paper presented at Proceedings of the International Conference on Image Analysis and Recognition ICIAR, 3/01/01, pp. 283-294.
- Experimental comparison of cluster ensemble methods.Kuncheva, LI, Hadjitodorov, ST & Todorova, LP 2006, 'Experimental comparison of cluster ensemble methods.' Paper presented at Proceedings of FUSION 2006, 3/01/01, .

#### 2005

- An ensemble-based method for linear feature extraction for two-class problems.Masip, D, Kuncheva, LI & Vitria, J 2005, 'An ensemble-based method for linear feature extraction for two-class problems.'
*Pattern Analysis and Applications*, vol 8, no. 3, pp. 227-237. DOI: 10.1007/s10044-005-0002-x - Diversity in multiple classifier systems.Kuncheva, LI 2005, 'Diversity in multiple classifier systems.'
*Information Fusion*, vol 6, no. 1, pp. 3-4. - Pattern Recognition.Kuncheva, LI, Everitt, BS (ed.) & Howell, D (ed.) 2005, Pattern Recognition. in
*Encyclopedia of Statistics in Behavioral Science.*2005 edn, vol. 3, Wiley, pp. 1532-1535. - Selection of independent binary features using probabilities: An example from veterinary medicineKuncheva, LI, Hoare, Z & Cockcroft, PD 2005, 'Selection of independent binary features using probabilities: An example from veterinary medicine'
*Journal of Modern Applied Statistical Methods*, vol 4, no. 2, pp. 528-537. - Using diversity measures for generating error-correcting output codes in classifier ensemblesKuncheva, LI 2005, 'Using diversity measures for generating error-correcting output codes in classifier ensembles'
*Pattern Recognition Letters*, vol 26, no. 1, pp. 83-90. DOI: 10.1016/j.patrec.2004.08.019

#### 2004

- A logodds criterion for selection of diagnostic tests.Whitaker C.J., NV, Kuncheva, LI & Cockcroft, PD 2004, 'A logodds criterion for selection of diagnostic tests.' Paper presented at Proceedings of the IAPR International Workshop on Statistical Pattern Recognition, 3/01/01, pp. 575-582.
- Classifier ensembles for changing environments.Kuncheva, LI 2004, 'Classifier ensembles for changing environments.' Paper presented at Proceedings of the 5th International Workshop on Multiple Classifier Systems, MCS2004, 3/01/01, pp. 1-15.
- Combining Pattern Classifiers: Methods and Algorithms.Kuncheva, LI 2004,
*Combining Pattern Classifiers: Methods and Algorithms.*Wiley. - Pre-selection of independent binary features: An application to diagnosing scrapie in sheepKuncheva, LI, Cockcroft, PD, Whitaker, CJ & Hoare, Z 2004, 'Pre-selection of independent binary features: An application to diagnosing scrapie in sheep' Paper presented at Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, 3/01/01, pp. 325-332.
- Using diversity in cluster ensembles.Kuncheva, LI & Hadjitodorov, ST 2004, 'Using diversity in cluster ensembles.' Paper presented at Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 3/01/01, pp. 1214-1219.

#### 2003

- `Fuzzy' vs `Non-fuzzy' in combining classifiers designed by boosting.Kuncheva, LI 2003, '`Fuzzy' vs `Non-fuzzy' in combining classifiers designed by boosting.'
*IEEE Transactions on Fuzzy Systems*, vol 11, no. 6, pp. 729-741. DOI: 10.1109/TFUZZ.2003.819842 - Marine DGNSS availability and continuity.Grant, A, Last, D, Kuncheva, LI & Ward, N 2003, 'Marine DGNSS availability and continuity.'
*Journal of Navigation*, vol 56, no. 3, pp. 353-369. DOI: 10.1017/S037346330300239X - Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy.Kuncheva, LI & Whitaker, CJ 2003, 'Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy.'
*Machine Learning*, vol 51, no. 2, pp. 181-207. DOI: 10.1023/A:1022859003006 - Limits on the majority vote accuracy in classifier fusion.Kuncheva, LI, Whitaker, CJ, Shipp, CA & Duin, RP 2003, 'Limits on the majority vote accuracy in classifier fusion.'
*Pattern Analysis and Applications*, vol 6, no. 1, pp. 22-31. DOI: 10.1007/s10044-002-0173-7 - An experimental study on diversity for bagging and boosting with linear classifiersKuncheva, LI, Skurichina, M & Duin, RP 2003, 'An experimental study on diversity for bagging and boosting with linear classifiers'
*Information Fusion*, vol 3, no. 2, pp. 245-258. - Error bounds for aggressive and conservative AdaBoost.Kuncheva, LI 2003, 'Error bounds for aggressive and conservative AdaBoost.' Paper presented at Proceedings MCS 2003, 3/01/01, pp. 25-34.
- Examining the relationship between majority vote accuracy and diversity in bagging and boostingWhitaker, CJ & Kuncheva, LI 2003, 'Examining the relationship between majority vote accuracy and diversity in bagging and boosting' Paper presented at Technical Report, 3/01/01, .
- Relationships between combination methods and measures of diversity in combining classifiersShipp, CA & Kuncheva, LI 2003, 'Relationships between combination methods and measures of diversity in combining classifiers'
*Information Fusion*, vol 3, no. 2, pp. 135-148. - That elusive diversity in classifier ensembles.Kuncheva, LI 2003, 'That elusive diversity in classifier ensembles.'
*Pattern Recognitions and Image Analysis: Proceedings*, vol 2652, pp. 1126-1138.

#### 2002

- Switching between selection and fusion in combining classifiers: An experimentKuncheva, LI 2002, 'Switching between selection and fusion in combining classifiers: An experiment'
*IEEE Transactions on SMC, Part B*, vol 32, no. 2, pp. 146-156. DOI: 10.1109/3477.990871 - Generating classifier outputs of fixed accuracy and diversity.Kuncheva, LI & Kountchev, RK 2002, 'Generating classifier outputs of fixed accuracy and diversity.'
*Pattern Recognition Letters*, vol 23, no. 5, pp. 593-600. - A theoretical study on six classifier fusion strategies.Kuncheva, LI 2002, 'A theoretical study on six classifier fusion strategies.'
*IEEE Transactions on Pattern Analysis and Machine Intelligence*, vol 24, no. 2, pp. 281-286. DOI: 10.1109/34.982906 - An investigation into how AdaBoost affects classifier diversity.Shipp, CA & Kuncheva, LI 2002, 'An investigation into how AdaBoost affects classifier diversity.' Paper presented at Proceedings IPMU 2002, 3/01/01, pp. 203-208.
- Bagging and Boosting for the nearest mean classifier: Effects of sample size on diversity and accuracySkurichina, M, Kuncheva, LI & Duin, RP 2002, 'Bagging and Boosting for the nearest mean classifier: Effects of sample size on diversity and accuracy' Paper presented at Proceedings MCS 2002, 3/01/01, pp. 62-71.
- Using diversity with three variants of boosting: aggressive, conservative and inverseKuncheva, LI & Whitaker, CJ 2002, 'Using diversity with three variants of boosting: aggressive, conservative and inverse' Paper presented at Proceedings MCS 2002, 3/01/01, pp. 81-90.

#### 2001

- Nearest prototype classifier designs: An experimental study.Bezdek, JC & Kuncheva, LI 2001, 'Nearest prototype classifier designs: An experimental study.'
*International Journal of Intelligent Systems*, vol 16, no. 12, pp. 1445-1473. - Using measures of similarity and inclusion for multiple classifier fusion by decision templatesKuncheva, LI 2001, 'Using measures of similarity and inclusion for multiple classifier fusion by decision templates'
*Fuzzy Sets and Systems*, vol 122, no. 3, pp. 401-407. - Decision templates for multiple classifier fusion: an experimental comparison.Kuncheva, LI, Bezdek, JC & Duin, RP 2001, 'Decision templates for multiple classifier fusion: an experimental comparison.'
*Pattern Recognition*, vol 34, no. 2, pp. 299-314. - A fuzzy model of heavy metal loadings in marine environment.Kuncheva, LI, Wrench, J, Jain, LC, Al-Zaidan, AS, Ruan, D (ed.), Kacprzyk, J (ed.) & Fedrizzi, M (ed.) 2001, A fuzzy model of heavy metal loadings in marine environment. in
*Soft Computing for Risk Assessment and Management.*2001 edn, Unknown, pp. 355-371. - Combining classifiers: Soft computing solutions.Kuncheva, LI, Pal, SK (ed.) & Pal, A (ed.) 2001, Combining classifiers: Soft computing solutions. in
*Pattern Recognition: From Classical to Modern Approaches.*2001 edn, Unknown, pp. 427-452. - Complexity of data subsets generated by the random subspace method: An experimental investigationKuncheva, LI, Roli, F, Marcialis, GL & Shipp, CA 2001, 'Complexity of data subsets generated by the random subspace method: An experimental investigation' Paper presented at MCS 2001, 3/01/01, pp. 349-358.
- Feature subsets for classifier combination: An enumerative experiment.Kuncheva, LI & Whitaker, CJ 2001, 'Feature subsets for classifier combination: An enumerative experiment.' Paper presented at MCS 2001, 3/01/01, pp. 228-237.
- Four measures of data complexity for bootstrapping, splitting and feature samplingShipp, CA & Kuncheva, LI 2001, 'Four measures of data complexity for bootstrapping, splitting and feature sampling' Paper presented at Proceedings of CIMA 2001, 3/01/01, pp. 429-435.
- Ten measures of diversity in classifier ensembles: Limits for two classifiers.Kuncheva, LI & Whitaker, CJ 2001, 'Ten measures of diversity in classifier ensembles: Limits for two classifiers.' Paper presented at IEE Workshop on Intelligent Sensor Processing, 3/01/01, .
- Using fuzzy similarities to analyze heavy metal distribution in a marine environmentAl-Zaidan, AS & Kuncheva, LI 2001, 'Using fuzzy similarities to analyze heavy metal distribution in a marine environment' Paper presented at Proceedings of CIMA 2001, 3/01/01, pp. 725-731.
- `Fuzzy' vs `non-fuzzy' in combining classifiers: an experimental study.Kuncheva, LI 2001, '`Fuzzy' vs `non-fuzzy' in combining classifiers: an experimental study.' Paper presented at Proceedings LFA '01, 3/01/01, pp. 11-22.