Efficient prediction algorithms for binary decomposition techniques

Data Mining and Knowledge Discovery

Volumn 24, Issue 1, 2012, Pages 40-77

  Park, Sang Hyeun ^a   Fürnkranz, Johannes ^a  

^a DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

Aggregation; Binary decomposition; Efficient decoding; Efficient voting; Multiclass classification; Pairwise classification; Ternary ECOC

Indexed keywords

BINARY DECOMPOSITIONS; EFFICIENT VOTING; MULTI-CLASS CLASSIFICATION; PAIRWISE CLASSIFICATION; TERNARY ECOC;

AGGLOMERATION; DECODING; LEARNING ALGORITHMS;

TEACHING;

EID: 84856600382     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-011-0219-9     Document Type: Article

References (41)

1. EL Allwein RE Schapire Y Singer 2000 Reducing multiclass to binary: a unifying approach for margin classifiers J Mach Learn Res 1 113 141 1884092
2. RC Bose DK Ray-Chaudhuri 1960 On a class of error correcting binary group codes Inform Control 3 1 68 79 10.1016/S0019-9958(60)90287-4 0104.36402 112768
3. SE Brenner P Koehl M Levitt 2000 The astral compendium for protein structure and sequence analysis Nucleic Acids Res 28 1 254 256 10.1093/nar/28.1.254
4. JS Cardoso JFP da Costa 2007 Learning to classify ordinal data: the data replication method J Mach Learn Res 8 1393 1429 1222.68157 2332436
5. K Crammer Y Singer 2002 On the learnability and design of output codes for multiclass problems Mach Learn 47 2-3 201 233 10.1023/A:1013637720281 1012.68155
6. Cutzu F (2003a) How to do multi-way classification with two-way classifiers. In: Kaynak O, Alpaydin E, Oja E, Xu L (eds) Artificial neural networks and neural information processing-ICANN/ICONIP 2003, joint international conference ICANN/ICONIP 2003, Istanbul. Lecture notes in computer science, vol 2714. Springer, Heidelberg, pp 375-384
7. Cutzu F (2003b) Polychotomous classification with pairwise classifiers: a new voting principle. In: Windeatt T, Roli F (eds) Multiple classifier systems, 4th international workshop (MCS 2003), Guilford. Lecture notes in computer science, vol 2709. Springer, Heidelberg, pp 115-124
8. TG Dietterich G Bakiri 1995 Solving multiclass learning problems via error-correcting output codes J Artif Intell Res 2 263 286 0900.68358
9. DL Donoho 2006 Compressed sensing IEEE Trans Inform Theory 52 4 1289 1306 10.1109/TIT.2006.871582 2241189
10. Escalera S, Pujol O, Radeva P (2006) Decoding of ternary error correcting output codes. In: Trinidad JFM, Carrasco-Ochoa JA, Kittler J (eds) Proceedings of the 11th Iberoamerican congress in pattern recognition (CIARP-06). Springer, Heidelberg, pp 753-763
11. S Escalera O Pujol P Radeva 2010 On the decoding process in ternary error-correcting output codes IEEE Trans Pattern Anal Mach Intell 32 1 120 134 10.1109/TPAMI.2008.266
12. Frank A, Asuncion A (2010) UCI machine learning repository. University of California, Irvine
13. J Fürnkranz 2002 Round robin classification J Mach Learn Res 2 721 747 1033.68086 1949973
14. J Fürnkranz 2003 Round robin ensembles Intell Data Anal 7 5 385 403
15. Gallager RG (1968) Information theory and reliable communication. Wiley, New York
16. Ghani R (2001) Using error-correcting codes for efficient text classification with a large number of categories. Master's thesis, Center for Automated Learning and Discovery, Carnegie Mellon University
17. Hastie T, Tibshirani R (1997) Classification by pairwise coupling. In: Jordan MI, Kearns MJ, Solla SA (eds) Advances in neural information processing systems 10 (NIPS 1997). MIT, Cambridge
18. CW Hsu CJ Lin 2002 A comparison of methods for multi-class support vector machines IEEE Trans Neural Netw 13 2 415 425 10.1109/72.991427
19. Hsu D, Kakade S, Langford J, Zhang T (2009) Multi-label prediction via compressed sensing. In: Bengio Y, Schuurmans D, Lafferty J, Williams CKI, Culotta A (eds) Advances in neural information processing systems 22, pp 772-780
20. Hüllermeier E, Fürnkranz J (2004a) Comparison of ranking procedures in pairwise preference learning. In: Proceedings of the 10th international conference on information processing and management of uncertainty in knowledge-based systems (IPMU-04), Perugia
21. Hüllermeier E, Fürnkranz J (2004b) Ranking by pairwise comparison: a note on risk minimization. In: Proceedings of the IEEE iInternational conference on fuzzy systems (FUZZ-IEEE-04), Budapest
22. E Hüllermeier J Fürnkranz W Cheng K Brinker 2008 Label ranking by learning pairwise preferences Artif Intell 172 16-17 1897 1916 10.1016/j.artint.2008.08.002 1184.68403
23. Kong EB, Dietterich TG (1995) Error-correcting output coding corrects bias and variance. In: Proceedings of the twelfth international conference on machine learning. Morgan Kaufmann, San Francisco, pp 313-321
24. DD Lewis Y Yang TG Rose F Li 2004 Rcv1: a new benchmark collection for text categorization research J Mach Learn Res 5 361 397
25. AC Lorena ACPLF de Carvalho J Gama 2008 A review on the combination of binary classifiers in multiclass problems Artif Intell Rev 30 1-4 19 37 10.1007/s10462-009-9114-9
26. MacWilliams FJ, Sloane NJA (1983) The theory of error-correcting codes. North-Holland Mathematical Library, North Holland
27. I Melvin E Ie J Weston WS Noble C Leslie 2007 Multi-class protein classification using adaptive codes J Mach Learn Res 8 1557 1581 1222.92031 2332441
28. EL Mencía SH Park J Fürnkranz 2010 Efficient voting prediction for pairwise multilabel classification Neurocomputing 73 7-9 1164 1176 10.1016/j.neucom.2009.11.024
29. AG Murzin SE Brenner T Hubbard C Chothia 1995 Scop: a structural classification of proteins database for the investigation of sequences and structures J Mol Biol 247 536 540
30. Park SH, Fürnkranz J (2007a) Efficient pairwise classification. In: Kok JN, Koronacki J, Lopez de Mantaras R, Matwin S, Mladenič D, Skowron A (eds) Proceedings of 18th European conference on machine learning (ECML-07), Warsaw. Springer-Verlag, Berlin, pp 658-665
31. Park SH, Fürnkranz J (2007b) Efficient pairwise classification and ranking. Technical Report TUD-KE-2007-03, Knowledge Engineering Group. TU Darmstadt
32. Park SH, Fürnkranz J (2009) Efficient decoding of ternary error-correcting output codes for multiclass classification. In: Buntine WL, Grobelnik M, Mladenič D, Shawe-Taylor J (eds) Proceedings of 20th European conference on machine learning (ECML-09), Bled. Springer-Verlag, Berlin, pp 189-204
33. E Pimenta J Gama Ferreira de Leon ACP de Carvalho 2008 The dimension of ECOCs for multiclass classification problems Int J Artif Intell Tools 17 3 433 447 10.1142/S0218213008003984
34. Platt JC, Cristianini N, Shawe-Taylor J (1999) Large margin DAGs for multiclass classification. In: Solla SA, Leen TK, Müller KR (eds) Advances in neural information processing systems 12 (NIPS 1999). The MIT Press, Denver, pp 547-553
35. Pujol O, Radeva P, Vitrià J (2006) Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes. IEEE Trans Pattern Anal Mach Intell 28(6):10071-1012
36. RM Rifkin A Klautau 2004 In defense of one-vs-all classification J Mach Learn Res 5 101 141 1222.68287 2247975
37. Smith RS, Windeatt T (2005) Decoding rules for error correcting output code ensembles. In: Oza NC, Polikar R, Kittler J, Roli F (eds) Proceedings of the 6th international workshop on multiple classifier systems (MCS-05), Seaside. Springer, New York, pp 53-63
38. T Windeatt R Ghaderi 2003 Coding and decoding strategies for multi-class learning problems Inform Fusion 4 1 11 21 10.1016/S1566-2535(02)00101-X
39. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco
40. T-F Wu CJ Lin RC Weng 2004 Probability estimates for multi-class classification by pairwise coupling J Mach Learn Res 5 975 1005 1222.68336 2248006
41. Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. In: Fisher DH (ed) Proceedings of the fourteenth international conference on machine learning (ICML 1997), Nashville. Morgan Kaufmann, San Francisco, pp 412-420