Adaptive prototype-based fuzzy classification

Fuzzy Sets and Systems

Volumn 159, Issue 21, 2008, Pages 2806-2818

  Cebron, Nicolas ^a   Berthold, Michael R ^a  

^a UNIVERSITY OF KONSTANZ   (Germany)

Author keywords

Active learning; Cell assays; Classification; Fuzzy clustering; Image mining; Noise handling

Indexed keywords

BIOINFORMATICS; CHLORINE COMPOUNDS; EDUCATION; FLOW OF SOLIDS; FOOD PROCESSING; FUZZY CLUSTERING; FUZZY SYSTEMS; IMAGE ENHANCEMENT; LABELING; LABELS; VECTOR QUANTIZATION;

A-PRIORI; ACTIVE LEARNING; CELL ASSAYS; CLASSIFICATION; CLASSIFICATION TECHNIQUES; DATA-SETS; FUZZY C-MEANS CLUSTERING; FUZZY CLASSIFICATIONS; HIGH THROUGHPUT SCREENINGS; IMAGE MINING; LARGE DATASETS; LEARNING PROCESSES; LEARNING VECTOR QUANTIZATION); NOISE HANDLING; SELECTED EXAMPLES;

CLASSIFICATION (OF INFORMATION);

BIOTECHNOLOGY; CHLORINE COMPOUNDS; CLASSIFICATION; DATA BASES; EDUCATION; FUZZY LOGIC; IMAGE ANALYSIS; LABELS;

EID: 50149118576     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fss.2008.03.019     Document Type: Article

References (24)

1. Angluin D. Queries and concept learning. Mach. Learn. 2 3 (1988) 319-342
2. Basu S., Banerjee A., and Mooney R.J. Active semi-supervision for pairwise constrained clustering. In: Berry M.W., Dayal U., Kamath C., and Skillicorn D.B. (Eds). SDM (2004), SIAM, Philadelphia, PA
3. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Plenum Press, New York
4. C.L. Blake, D.J. Newman, S. Hettich, C.J. Merz, UCI repository of machine learning databases, 1998.
5. Cohn D., Ghahramani Z., and Jordan M. Active learning with statistical models. Adv. in Neural Inform. Process. Syst. 7 (1995) 705-712
6. Cohn D.A., Atlas L., and Ladner R.E. Improving generalization with active learning. Mach. Learn. 15 2 (1994) 201-221
7. S. Dasgupta, Analysis of a greedy active learning strategy, In: NIPS, 2004.
8. Dave R.N. Characterization and detection of noise in clustering. Pattern Recognition Lett. 12 11 (1991) 657-664
9. Freund Y., Seung H.S., Shamir E., and Tishby N. Selective sampling using the query by committee algorithm. Mach. Learn. 28 2-3 (1997) 133-168
10. Gabrys B., and Petrakieva L. Combining labelled and unlabelled data in the design of pattern classification systems. Internat. J. Approx. Reason. 35 3 (2004) 251-273
11. Grira N., Crucianu M., and Boujemaa N. Active semi-supervised fuzzy clustering for image database categorization. In: Zhang H., Smith J., and Tian Q. (Eds). Multimedia Information Retrieval (2005), ACM 9-16
12. Haralick R.M., Shanmugam K., and Dinstein I. Textural features for image classification. SMC 3 6 (1973) 610-621
13. Hochbaum D.S., and Shmoys D.B. A best possible heuristic for the k-center problem. Math. Oper. Res. 10 2 (1985) 180-184
14. J. Jantzen, et al., Pap-smear benchmark data for pattern classification 〈http://fuzzy.iau.dtu.dk/downloads/smear2005/〉, 2005.
15. T.R. Jones, A. Carpenter, P. Golland, Voronoi-based segmentation of cells on image manifolds, in: Y. Liu, T. Jiang, C. Zhang (Eds.), CVBIA, in: Lecture Notes in Computer Science, Vol. 3765, Springer, Berlin, 2005, pp. 535-543.
16. Kirkpatrick S., Gelatt Jr. C.D., and Vecchi M.P. Optimization by simulated annealing. Science 220 4598 (1983) 671-680
17. Kohonen T. The self-organizing map. Neurocomputing 21 1-3 (1998) 1-6
18. Luo T., Kramer K., Goldgof D.B., Hall L.O., Samson S., Remsen A., and Hopkins T. Active learning to recognize multiple types of plankton. J. Mach. Learn. Res. 6 (2005) 589-613
19. Nguyen H.T., and Smeulders A. Active learning using pre-clustering. In: Brodley C.E. (Ed). ICML (2004), ACM
20. Schohn G., and Cohn D. Less is more: active learning with support vector machines. In: Langley P. (Ed). ICML (2000), Morgan Kaufmann, Los Altos, CA 839-846
21. L. Wang, K.L. Chan, Z.H. Zhang, Bootstrapping svm active learning by incorporating unlabelled images for image retrieval, in: Proc. IEEE Comput. Soc. Conf. on Computer Vision and Pattern Recognition, Vol. 1, 2003, pp. 629-634.
22. Warmuth M.K., Liao J., Rätsch G., Mathieson M., Putta S., and Lemmen C. Active learning with support vector machines in the drug discovery process. J. Chem. Inform. Comput. Sci. 43 2 (2003) 667-673
23. Windham M.P. Cluster validity for fuzzy clustering algorithms. Fuzzy Sets and Systems 5 (1981) 177-185
24. Zernike F. Diffraction theory of the cut procedure and its improved form, the phase contrast method. Physica 1 (1934) 689-704