A scalable noise reduction technique for large case-based systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5650 LNAI, Issue , 2009, Pages 328-342

  Segata, Nicola ^a   Blanzieri, Enrico ^a   Cunningham, Pádraig ^b  

^a UNIVERSITY OF TRENTO   (Italy)

^b UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

CBR; DECISION BOUNDARY; EXCELLENT PERFORMANCE; LARGE DATASETS; LEARNING TECHNIQUES; NOISE REDUCTION TECHNIQUE; NOISE REDUCTIONS; NOISE-TOLERANT; NOISY DATA; TRAINING SAMPLE;

ACOUSTIC NOISE MEASUREMENT; BITS; DATA REDUCTION; DECISION TREES; LEARNING ALGORITHMS; SUPPORT VECTOR MACHINES;

CASE BASED REASONING;

EID: 70350759438     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02998-1_24     Document Type: Conference Paper

References (38)

1. Leake, D.B.: CBR in context: The present and future. In: Leake (ed.) Case Based Reasoning: Experiences, Lessons, and Future Directions, pp. 3-30. MIT Press, Cambridge (1996)
2. Cunningham, P., Doyle, D., Loughrey, J.: An evaluation of the usefulness of case-based explanation. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 122-130. Springer, Heidelberg (2003)
3. Lorena, A.C., Carvalho, A.: Evaluation of noise reduction techniques in the splice junction recognition problem. Genet. Mol. Biol. 27, 665-672 (2004)
4. Devijver, P., Kittler, J.: Pattern recognition: a statistical approach, Englewood Cliffs, London (1982)
5. Segata, N., Blanzieri, E., Delany, S., Cunningham, P.: Noise reduction for instance-based learning with a local maximal margin approach. Technical Report DISI-08-056, DISI, University of Trento, Italy (2008)
6. Blanzieri, E., Melgani, F.: Nearest neighbor classification of remote sensing images with the maximal margin principle. IEEE Trans. Geosci. Remote Sens. 46(6) (2008)
7. Segata, N., Blanzieri, E.: Empirical assessment of classification accuracy of Local SVM. In: Proc. of Benelearn, pp. 47-55 (2009)
8. Segata, N.: FaLKM-lib v1.0: a Library for Fast Local Kernel Machines. Technical report, DISI, University of Trento, Italy (2009), http://disi.unitn. it/~segata/FaLKM-lib
9. Cataltepe, Z., Abu-mostafa, Y.S., Magdon-ismail, M.: No free lunch for early stopping. Neural Comput. 11, 995-1009 (1999)
10. Quinlan, J.: The effect of noise on concept learning. In: Michalski, R., Carboneel, J., Mitchell, T. (eds.) Mach Learn. Morgan Kaufmann, San Francisco (1986)
11. Cortes, C., Vapnik, V.: Support-vector networks. Mach Learn., 273-297 (1995)
12. Roth-Berghofer, T.: Explanations and case-based reasoning: Foundational issues. In: Funk, P., González-Calero, P. (eds.) ECCBR 2004. LNCS, vol. 3155, pp. 389-403. Springer, Heidelberg (2004)
13. Nugent, C., Doyle, D., Cunningham, P.: Gaining insight through case-based explanation. Int. J. Intell. Inf. Syst. (2008)
14. Pechenizkiy, M., Tsymbal, A., Puuronen, S., Pechenizkiy, O.: Class noise and supervised learning in medical domains: The effect of feature extraction. In: CBMS 2006, Washington, DC, USA, pp. 708-713. IEEE Computer Society, Los Alamitos (2006)
15. Malossini, A., Blanzieri, E., Ng, R.T.: Detecting potential labeling errors in mi-croarrays by data perturbation. Bioinformatics 22(17), 2114-2121 (2006)
16. Gamberger, A., Lavrac, N., Dzeroski, S.: Noise detection and elimination in data preprocessing: experiments in medical domains. Appl. Artif. Intell., 205-223 (2000)
17. Tang, S., Chen, S.P.: Data cleansing based on mathematic morphology. In: iCBBE 2008, pp. 755-758 (2008)
18. Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Mach. Learn. 38(3), 257-286 (2000)
19. Brighton, H., Mellish, C.: Advances in instance selection for instance-based learning algorithms. Data Min. Knowl. Discovery 6(2), 153-172 (2002)
20. Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans. Syst. Man Cybern. 2(3), 408-421 (1972)
21. Tomek, I.: An experiment with the edited nearest-neighbor rule. IEEE Trans. Syst. Man Cybern. 6(6), 448-452 (1976)
22. Koplowitz, J., Brown, T.A.: On the relation of performance to editing in nearest neighbor rules. Pattern Recognit. 13(3), 251-255 (1981)
23. Jiang, Y., Zhou, Z.: Editing training data for knn classifiers with neural network ensemble. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3173, pp. 356-361. Springer, Heidelberg (2004)
24. Sánchez, J.S., Barandela, R., Marqués, A.I., Alejo, R., Badenas, J.: Analysis of new techniques to obtain quality training sets. Pattern Recognit. Lett. 24(7) (2003)
25. Delany, S.J., Cunningham, P.: An analysis of case-base editing in a spam filtering system. In: Funk, P., González Calero, P. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 128-141. Springer, Heidelberg (2004)
26. Pan, R., Yang, Q., Pan, S.J.: Mining competent case bases for case-based reasoning. Artif. Intell. 171(16-17), 1039-1068 (2007)
27. Angiulli, F.: Fast nearest neighbor condensation for large data sets classification. IEEE Trans. Knowl. Data Eng. 19(11), 1450-1464 (2007)
28. Bottou, L., Vapnik, V.: Local learning algorithms. Neural Comput. 4(6) (1992)
29. Vapnik, V.N., Bottou, L.: Local algorithms for pattern recognition and dependencies estimation. Neural Comput. 5(6), 893-909 (1993)
30. Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Adv. in Large Margin Classifiers, pp. 61-74 (1999)
31. Lin, H.T., Lin, C.J., Weng, R.: A note on Platt's probabilistic outputs for support vector machines. Mach. Learn. 68(3), 267-276 (2007)
32. Beygelzimer, A., Kakade, S., Langford, J.: Cover Trees for Nearest Neighbor. In: ICML 2006, pp. 97-104. ACM Press, New York (2006)
33. Krauthgamer, R., Lee, J.: Navigating nets: simple algorithms for proximity search. In: SODA 2004, Society for Industrial and Applied Mathematics, pp. 798-807 (2004)
34. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001)
35. Asuncion, A., Newman, D.J.: Uci machine learning repository (2007)
36. Segata, N., Blanzieri, E.: Fast local support vector machines for large datasets. In: Proc. of MLDM (2009) (accepted for publication)
37. Uzilov, A., Keegan, J., Mathews, D.: Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change. BMC Bioinf. 7(1), 173 (2006)
38. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1-30 (2006)