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Ludmila I. Kuncheva
Ludmila Kuncheva
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2020 – today
- 2024
- [j72]Ludmila I. Kuncheva, José Luis Garrido-Labrador
, Ismael Ramos-Pérez
Semi-supervised classification with pairwise constraints: A case study on animal identification from video. Inf. Fusion 104: 102188 (2024) - [c55]Samuel L. Hennessey, Francis J. Williams, Ludmila I. Kuncheva:
Hierarchical Vs Centroid-Based Constraint Clustering for Animal Video Data. IS 2024: 1-6 - [c54]Francis J. Williams, Samuel L. Hennessey, Ludmila I. Kuncheva, José-Francisco Díez-Pastor, Juan J. Rodriguez:
A Constrained Cluster Ensemble Using Hierarchical Clustering Methods. IS 2024: 1-6 - 2023
- [j71]Ludmila I. Kuncheva, José Luis Garrido-Labrador
An experiment on animal re-identification from video. Ecol. Informatics 74: 101994 (2023) - 2022
- [c53]Ludmila I. Kuncheva, Francis J. Williams, Samuel L. Hennessey, Juan José Rodríguez
A Benchmark Database for Animal Re-Identification and Tracking. IPAS 2022: 1-6 - [c52]Francis J. Williams, Ludmila I. Kuncheva, Juan José Rodríguez
Combination of Object Tracking and Object Detection for Animal Recognition. IPAS 2022: 1-6 - [i8]Ludmila Kuncheva, Francis J. Williams, Samuel L. Hennessey:
A Bibliographic View on Constrained Clustering. CoRR abs/2209.11125 (2022) - 2021
- [j70]Ludmila Kuncheva:
Animal reidentification using restricted set classification. Ecol. Informatics 62: 101225 (2021) - 2020
- [j69]Juan José Rodríguez
An experimental evaluation of mixup regression forests. Expert Syst. Appl. 151: 113376 (2020) - [j68]Juan José Rodríguez
Random Balance ensembles for multiclass imbalance learning. Knowl. Based Syst. 193: 105434 (2020) - [c51]Julian Zubek, Ludmila Kuncheva:
Abstraction and Generalization: Comparing Adaptive Models of Categorization. CogSci 2020 - [i7]Ludmila I. Kuncheva, Clare E. Matthews, Álvar Arnaiz-González, Juan José Rodríguez
Feature Selection from High-Dimensional Data with Very Low Sample Size: A Cautionary Tale. CoRR abs/2008.12025 (2020)
2010 – 2019
- 2019
- [j67]William J. Faithfull
Combining univariate approaches for ensemble change detection in multivariate data. Inf. Fusion 45: 202-214 (2019) - [j66]Clare E. Matthews
Classification and comparison of on-line video summarisation methods. Mach. Vis. Appl. 30(3): 507-518 (2019) - [j65]Ludmila I. Kuncheva
Instance selection improves geometric mean accuracy: a study on imbalanced data classification. Prog. Artif. Intell. 8(2): 215-228 (2019) - [i6]Iain A. D. Gunn, Ludmila I. Kuncheva:
Bounds for the VC Dimension of 1NN Prototype Sets. CoRR abs/1902.02660 (2019) - 2018
- [j64]Iain A. D. Gunn, Álvar Arnaiz-González
A taxonomic look at instance-based stream classifiers. Neurocomputing 286: 167-178 (2018) - [j63]Ludmila I. Kuncheva
Edited nearest neighbour for selecting keyframe summaries of egocentric videos. J. Vis. Commun. Image Represent. 52: 118-130 (2018) - [j62]Ludmila I. Kuncheva
On feature selection protocols for very low-sample-size data. Pattern Recognit. 81: 660-673 (2018) - [j61]Ludmila I. Kuncheva
Restricted Set Classification with prior probabilities: A case study on chessboard recognition. Pattern Recognit. Lett. 111: 36-42 (2018) - [c50]Paria Yousefi, Ludmila I. Kuncheva
Selective Keyframe Summarisation for Egocentric Videos Based on Semantic Concept Search. IPAS 2018: 19-24 - [c49]Paria Yousefi
Budget-Constrained Online Video Summarisation of Egocentric Video Using Control Charts. ISVC 2018: 640-649 - [i5]Ludmila I. Kuncheva, Álvar Arnaiz-González, José-Francisco Díez-Pastor, Iain A. D. Gunn:
Instance Selection Improves Geometric Mean Accuracy: A Study on Imbalanced Data Classification. CoRR abs/1804.07155 (2018) - [i4]Julian Zubek, Ludmila Kuncheva:
Learning from Exemplars and Prototypes in Machine Learning and Psychology. CoRR abs/1806.01130 (2018) - 2017
- [j60]Ludmila I. Kuncheva
Restricted set classification: Who is there? Pattern Recognit. 63: 158-170 (2017) - [c48]Ludmila I. Kuncheva
Comparing keyframe summaries of egocentric videos: Closest-to-centroid baseline. IPTA 2017: 1-6 - [i3]Ludmila I. Kuncheva, Paria Yousefi, Iain A. D. Gunn:
On the Evaluation of Video Keyframe Summaries using User Ground Truth. CoRR abs/1712.06899 (2017) - [i2]Iain A. D. Gunn, Ludmila I. Kuncheva, Paria Yousefi:
Bipartite Graph Matching for Keyframe Summary Evaluation. CoRR abs/1712.06914 (2017) - 2016
- [c47]Ludmila Kuncheva:
Getting Lost in the Wealth of Classifier Ensembles? ICPRAM 2016: 7 - [c46]Ludmila I. Kuncheva
A concept-drift perspective on prototype selection and generation. IJCNN 2016: 16-23 - 2015
- [j59]José-Francisco Díez-Pastor, Juan José Rodríguez
Diversity techniques improve the performance of the best imbalance learning ensembles. Inf. Sci. 325: 98-117 (2015) - [j58]José-Francisco Díez-Pastor, Juan José Rodríguez Diez
Random Balance: Ensembles of variable priors classifiers for imbalanced data. Knowl. Based Syst. 85: 96-111 (2015) - [c45]Ludmila I. Kuncheva
Theoretical and Empirical Criteria for the Edited Nearest Neighbour Classifier. ICDM 2015: 817-822 - 2014
- [j57]Ludmila I. Kuncheva
A weighted voting framework for classifiers ensembles. Knowl. Inf. Syst. 38(2): 259-275 (2014) - [j56]Ludmila I. Kuncheva
A spatial discrepancy measure between voxel sets in brain imaging. Signal Image Video Process. 8(5): 913-922 (2014) - [j55]Javier Marín
Occlusion Handling via Random Subspace Classifiers for Human Detection. IEEE Trans. Cybern. 44(3): 342-354 (2014) - [j54]Ludmila I. Kuncheva
PCA Feature Extraction for Change Detection in Multidimensional Unlabeled Data. IEEE Trans. Neural Networks Learn. Syst. 25(1): 69-80 (2014) - [c44]Ludmila I. Kuncheva
Who Is Missing? A New Pattern Recognition Puzzle. S+SSPR 2014: 243-252 - [c43]William J. Faithfull, Ludmila I. Kuncheva
On Optimum Thresholding of Multivariate Change Detectors. S+SSPR 2014: 364-373 - 2013
- [j53]Ludmila I. Kuncheva
Interval feature extraction for classification of event-related potentials (ERP) in EEG data analysis. Prog. Artif. Intell. 2(1): 65-72 (2013) - [j52]Ludmila I. Kuncheva
A Bound on Kappa-Error Diagrams for Analysis of Classifier Ensembles. IEEE Trans. Knowl. Data Eng. 25(3): 494-501 (2013) - [j51]Ludmila I. Kuncheva
Change Detection in Streaming Multivariate Data Using Likelihood Detectors. IEEE Trans. Knowl. Data Eng. 25(5): 1175-1180 (2013) - 2012
- [j50]Ludmila I. Kuncheva
Classifier Ensemble Methods for Diagnosing COPD from Volatile Organic Compounds in Exhaled Air. Int. J. Knowl. Discov. Bioinform. 3(2): 1-15 (2012) - [j49]Catrin O. Plumpton
Naive random subspace ensemble with linear classifiers for real-time classification of fMRI data. Pattern Recognit. 45(6): 2101-2108 (2012) - [c42]Ludmila I. Kuncheva
Evaluation of Feature Ranking Ensembles for High-Dimensional Biomedical Data: A Case Study. ICDM Workshops 2012: 49-56 - [c41]Ludmila I. Kuncheva, William J. Faithfull:
PCA feature extraction for change detection in multidimensional unlabelled streaming data. ICPR 2012: 1140-1143 - [i1]Ludmila Kuncheva, Christopher J. Whitaker, Peter D. Cockcroft, Z. S. J. Hoare:
Pre-Selection of Independent Binary Features: An Application to Diagnosing Scrapie in Sheep. CoRR abs/1207.4141 (2012) - 2011
- [c40]Ludmila I. Kuncheva
Multi-modal Biometric Emotion Recognition Using Classifier Ensembles. IEA/AIE (1) 2011: 317-326 - 2010
- [j48]Ludmila I. Kuncheva
Full-class set classification using the Hungarian algorithm. Int. J. Mach. Learn. Cybern. 1(1-4): 53-61 (2010) - [j47]Robi Polikar
Learn++.MF: A random subspace approach for the missing feature problem. Pattern Recognit. 43(11): 3817-3832 (2010) - [j46]Ludmila I. Kuncheva
Random Subspace Ensembles for fMRI Classification. IEEE Trans. Medical Imaging 29(2): 531-542 (2010) - [c39]Alberto Dainotti
Identification of Traffic Flows Hiding behind TCP Port 80. ICC 2010: 1-6 - [c38]Catrin O. Plumpton
On-Line fMRI Data Classification Using Linear and Ensemble Classifiers. ICPR 2010: 4312-4315 - [c37]Ludmila I. Kuncheva
Choosing Parameters for Random Subspace Ensembles for fMRI Classification. MCS 2010: 54-63 - [c36]Gavin Brown, Ludmila I. Kuncheva
"Good" and "Bad" Diversity in Majority Vote Ensembles. MCS 2010: 124-133
2000 – 2009
- 2009
- [j45]Ludmila I. Kuncheva
On the window size for classification in changing environments. Intell. Data Anal. 13(6): 861-872 (2009) - [c35]Indre Zliobaite
Determining the Training Window for Small Sample Size Classification with Concept Drift. ICDM Workshops 2009: 447-452 - [c34]Francesco Gargiulo
Network Protocol Verification by a Classifier Selection Ensemble. MCS 2009: 314-323 - 2008
- [j44]J. J. Charles, Ludmila I. Kuncheva
Object segmentation within microscope images of palynofacies. Comput. Geosci. 34(6): 688-698 (2008) - [j43]Ludmila Kuncheva
Error-Dependency Relationships for the Naïve Bayes Classifier with Binary Features. IEEE Trans. Pattern Anal. Mach. Intell. 30(4): 735-740 (2008) - [j42]Ludmila I. Kuncheva
A case-study on naïve labelling for the nearest mean and the linear discriminant classifiers. Pattern Recognit. 41(10): 3010-3020 (2008) - [j41]Ludmila I. Kuncheva
Fuzzy classifiers. Scholarpedia 3(1): 2925 (2008) - [c33]Ludmila I. Kuncheva
Nearest Neighbour Classifiers for Streaming Data with Delayed Labelling. ICDM 2008: 869-874 - [c32]Ludmila I. Kuncheva
Linear Discriminant Classifier (LDC) for Streaming Data with Concept Drift. SSPR/SPR 2008: 4 - [c31]Ludmila I. Kuncheva
Adaptive Learning Rate for Online Linear Discriminant Classifiers. SSPR/SPR 2008: 510-519 - [c30]Juan José Rodríguez
Combining Online Classification Approaches for Changing Environments. SSPR/SPR 2008: 520-529 - [c29]J. J. Charles, Ludmila I. Kuncheva, B. Wells, Ik Soo Lim:
Background Segmentation in Microscopy Images. VISAPP (1) 2008: 139-145 - 2007
- [j40]Ludmila I. Kuncheva
Diagnosing scrapie in sheep: A classification experiment. Comput. Biol. Medicine 37(8): 1194-1202 (2007) - [j39]Ludmila I. Kuncheva
Classifier Ensembles with a Random Linear Oracle. IEEE Trans. Knowl. Data Eng. 19(4): 500-508 (2007) - [c28]Anand M. Narasimhamurthy, Ludmila I. Kuncheva:
A framework for generating data to simulate changing environments. Artificial Intelligence and Applications 2007: 415-420 - [c27]Ludmila I. Kuncheva:
A stability index for feature selection. Artificial Intelligence and Applications 2007: 421-427 - [c26]J. Salvador Sánchez, Ludmila I. Kuncheva:
Data reduction using classifier ensembles. ESANN 2007: 379-384 - [c25]Stefan Todorov Hadjitodorov, Ludmila I. Kuncheva
Selecting Diversifying Heuristics for Cluster Ensembles. MCS 2007: 200-209 - [c24]Juan José Rodríguez, Ludmila I. Kuncheva
Naïve Bayes Ensembles with a Random Oracle. MCS 2007: 450-458 - [c23]Ludmila I. Kuncheva
An Experimental Study on Rotation Forest Ensembles. MCS 2007: 459-468 - 2006
- [j38]Stefan Todorov Hadjitodorov, Ludmila I. Kuncheva
Moderate diversity for better cluster ensembles. Inf. Fusion 7(3): 264-275 (2006) - [j37]Juan José Rodríguez
Rotation Forest: A New Classifier Ensemble Method. IEEE Trans. Pattern Anal. Mach. Intell. 28(10): 1619-1630 (2006) - [j36]Ludmila I. Kuncheva
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization. IEEE Trans. Pattern Anal. Mach. Intell. 28(11): 1798-1808 (2006) - [j35]Ludmila I. Kuncheva
On the optimality of Naïve Bayes with dependent binary features. Pattern Recognit. Lett. 27(7): 830-837 (2006) - [j34]Fernando Vilariño
ROC curves and video analysis optimization in intestinal capsule endoscopy. Pattern Recognit. Lett. 27(8): 875-881 (2006) - [c22]Ludmila I. Kuncheva
Experimental Comparison of Cluster Ensemble Methods. FUSION 2006: 1-7 - [c21]J. J. Charles, Ludmila I. Kuncheva
An Evaluation Measure of Image Segmentation Based on Object Centres. ICIAR (1) 2006: 283-294 - 2005
- [j33]Ludmila I. Kuncheva
Diversity in multiple classifier systems. Inf. Fusion 6(1): 3-4 (2005) - [j32]David Masip
An ensemble-based method for linear feature extraction for two-class problems. Pattern Anal. Appl. 8(3): 227-237 (2005) - [j31]Ludmila I. Kuncheva
Using diversity measures for generating error-correcting output codes in classifier ensembles. Pattern Recognit. Lett. 26(1): 83-90 (2005) - 2004
- [b2]Ludmila I. Kuncheva
Combining Pattern Classifiers: Methods and Algorithms. Wiley 2004, ISBN 9780471210788 - [c20]Ludmila I. Kuncheva
Classifier Ensembles for Changing Environments. Multiple Classifier Systems 2004: 1-15 - [c19]Ludmila I. Kuncheva
Using diversity in cluster ensembles. SMC (2) 2004: 1214-1219 - [c18]Christopher J. Whitaker, Ludmila I. Kuncheva
A Logodds Criterion for Selection of Diagnostic Tests. SSPR/SPR 2004: 574-582 - [c17]Ludmila I. Kuncheva, Christopher J. Whitaker, Peter D. Cockcroft, Z. S. J. Hoare:
Pre-Selection of Independent Binary Features: An Application to Diagnosing Scrapie in. UAI 2004: 325-332 - 2003
- [j30]Ludmila I. Kuncheva
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy. Mach. Learn. 51(2): 181-207 (2003) - [j29]Ludmila I. Kuncheva
Limits on the majority vote accuracy in classifier fusion. Pattern Anal. Appl. 6(1): 22-31 (2003) - [j28]Ludmila I. Kuncheva
"Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting. IEEE Trans. Fuzzy Syst. 11(6): 729-741 (2003) - [c16]Ludmila Kuncheva
That Elusive Diversity in Classifier Ensembles. IbPRIA 2003: 1126-1138 - [c15]Ludmila I. Kuncheva
Error Bounds for Aggressive and Conservative AdaBoost. Multiple Classifier Systems 2003: 25-34 - 2002
- [j27]Catherine A. Shipp, Ludmila Kuncheva
Relationships between combination methods and measures of diversity in combining classifiers. Inf. Fusion 3(2): 135-148 (2002) - [j26]Ludmila Kuncheva
An experimental study on diversity for bagging and boosting with linear classifiers. Inf. Fusion 3(4): 245-258 (2002) - [j25]Ludmila Kuncheva
A Theoretical Study on Six Classifier Fusion Strategies. IEEE Trans. Pattern Anal. Mach. Intell. 24(2): 281-286 (2002) - [j24]Ludmila Kuncheva
Generating classifier outputs of fixed accuracy and diversity. Pattern Recognit. Lett. 23(5): 593-600 (2002) - [j23]Ludmila I. Kuncheva
Switching between selection and fusion in combining classifiers: an experiment. IEEE Trans. Syst. Man Cybern. Part B 32(2): 146-156 (2002) - [c14]Marina Skurichina, Ludmila Kuncheva
Bagging and Boosting for the Nearest Mean Classifier: Effects of Sample Size on Diversity and Accuracy. Multiple Classifier Systems 2002: 62-71 - [c13]Ludmila I. Kuncheva
Using Diversity with Three Variants of Boosting: Aggressive, Conservative, and Inverse. Multiple Classifier Systems 2002: 81-90 - 2001
- [j22]Ludmila Kuncheva
Using measures of similarity and inclusion for multiple classifier fusion by decision templates. Fuzzy Sets Syst. 122(3): 401-407 (2001) - [j21]James C. Bezdek, Ludmila Kuncheva
Nearest prototype classifier designs: An experimental study. Int. J. Intell. Syst. 16(12): 1445-1473 (2001) - [j20]Ludmila Kuncheva:
Fuzzy Logic with Engineering Applications, Timothy J. Ross, (Ed.); McGraw Hill, New York, 1995, pp 592, ISBN 0-07-053917-0. Neurocomputing 41(1-4): 187 (2001) - [j19]Ludmila Kuncheva
Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognit. 34(2): 299-314 (2001) - [c12]Ludmila I. Kuncheva
Feature Subsets for Classifier Combination: An Enumerative Experiment. Multiple Classifier Systems 2001: 228-237 - [c11]Ludmila I. Kuncheva
Complexity of Data Subsets Generated by the Random Subspace Method: An Experimental Investigation. Multiple Classifier Systems 2001: 349-358 - 2000
- [b1]Ludmila I. Kuncheva
Fuzzy Classifier Design. Studies in Fuzziness and Soft Computing 49, Springer 2000, ISBN 978-3-7908-2472-8, pp. 1-270 - [j18]Ludmila Kuncheva
A fuzzy model of heavy metal loadings in Liverpool bay. Environ. Model. Softw. 15(2-3): 161-167 (2000) - [j17]Ludmila Kuncheva
Designing classifier fusion systems by genetic algorithms. IEEE Trans. Evol. Comput. 4(4): 327-336 (2000) - [j16]Ludmila I. Kuncheva
How good are fuzzy If-Then classifiers? IEEE Trans. Syst. Man Cybern. Part B 30(4): 501-509 (2000) - [c10]Ludmila I. Kuncheva
Is Independence Good For Combining Classifiers? ICPR 2000: 2168-2171 - [c9]Ludmila I. Kuncheva
Clustering-and-selection model for classifier combination. KES 2000: 185-188 - [c8]Ameena S. Al-Zaidan, Ludmila I. Kuncheva
Selecting fuzzy connectives to represent heavy metal distribution in Liverpool Bay. KES 2000: 602-605 - [c7]James C. Bezdek, Ludmila Kuncheva
Some Notes on Twenty One (21) Nearest Prototype Classifiers. SSPR/SPR 2000: 1-16
1990 – 1999
- 1999
- [j15]Ludmila Kuncheva
Fuzzy diagnosis. Artif. Intell. Medicine 16(2): 121-128 (1999) - [j14]Ludmila Kuncheva
Nearest neighbor classifier: Simultaneous editing and feature selection. Pattern Recognit. Lett. 20(11-13): 1149-1156 (1999) - [j13]James C. Bezdek, James M. Keller, Raghu Krishnapuram, Ludmila I. Kuncheva
Will the real iris data please stand up? IEEE Trans. Fuzzy Syst. 7(3): 368-369 (1999) - [j12]Ludmila I. Kuncheva
Presupervised and post-supervised prototype classifier design. IEEE Trans. Neural Networks 10(5): 1142-1152 (1999) - 1998
- [j11]Ludmila I. Kuncheva
An Integrated Framework for Generalized Nearest Prototype Classifier Design. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 6(5): 437-458 (1998) - [j10]Ludmila I. Kuncheva
Nearest prototype classification: clustering, genetic algorithms, or random search? IEEE Trans. Syst. Man Cybern. Part C 28(1): 160-164 (1998) - 1997
- [j9]Ludmila Kuncheva
Initializing of an RBF network by a genetic algorithm. Neurocomputing 14(3): 273-288 (1997) - [j8]Ludmila I. Kuncheva
Fitness functions in editing k-NN reference set by genetic algorithms. Pattern Recognit. 30(6): 1041-1049 (1997) - 1996
- [j7]Ludmila I. Kuncheva
A fuzzy consensus aggregation operator. Fuzzy Sets Syst. 79(3): 347-356 (1996) - [j6]Ludmila I. Kuncheva
On the Equivalence between fuzzy and Statistical Classifiers. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 4(3): 245-254 (1996) - [c6]Ludmila Kuncheva
An RBF network with tunable function shape. ICPR 1996: 645-649 - 1995
- [j5]Ludmila I. Kuncheva
Editing for the k-nearest neighbors rule by a genetic algorithm. Pattern Recognit. Lett. 16(8): 809-814 (1995) - [c5]Ludmila Kuncheva
On Feature Selection via Rough Sets. CAIP 1995: 625-630 - [c4]Ludmila Kuncheva
Experimental Investigation on Editing for the k-NN Rule through a Genetic Algorithm. CAIP 1995: 766-771 - 1994
- [c3]Ludmila I. Kuncheva
A two-level classification scheme trained by a fuzzy neural network. ICPR (2) 1994: 467-469 - 1993
- [j4]Ludmila Kuncheva
Genetic Algorithm for Feature Selection for Parallel Classifiers. Inf. Process. Lett. 46(4): 163-168 (1993) - [j3]Ludmila I. Kuncheva
'Change-glasses' approach in pattern recognition. Pattern Recognit. Lett. 14(8): 619-623 (1993) - [c2]Ludmila Kuncheva
A combination Scheme of Artificial Intelligence and Fuzzy Pattern Recognition in Medical Diagnosis. FLAI 1993: 157-164 - 1992
- [j2]Ludmila I. Kuncheva
A critical comment on the paper "designing of classification procedures with the use of equality and difference operators". Pattern Recognit. 25(9): 1069-1071 (1992) - 1991
- [c1]Ludmila I. Kuncheva
A Decision Making System in Aviation Medicine. MIE 1991: 418-422 - 1990
- [j1]Vesselin Kissiov, Stefan Todorov Hadjitodorov, Ludmila I. Kuncheva
Using key features in pattern classification. Pattern Recognit. Lett. 11(1): 1-5 (1990)
Coauthor Index
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