Efficient decoding of ternary error-correcting output codes for multiclass classification

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

Volumn 5782 LNAI, Issue PART 2, 2009, Pages 189-204

  Park, Sang Hyeun ^a   Fürnkranz, Johannes ^a  

^a DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE DECODING; CLASSIFIER EVALUATION; CODE DESIGNS; DECODING STRATEGY; EMPIRICAL EVALUATIONS; ERROR CORRECTING OUTPUT CODE; MATRIX; MULTI-CLASS CLASSIFICATION; PERFORMANCE GAIN;

ADAPTIVE ALGORITHMS; FORECASTING; ROBOT LEARNING;

DECODING;

EID: 70349967931     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04174-7_13     Document Type: Conference Paper

References (16)

1. Allwein, E.L., Schapire, R.E., Singer, Y.: Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research 1, 113-141 (2000)
2. Asuncion, A., Newman, D.: UCI machine learning repository (2007)
3. Bose, R.C., Ray-Chaudhuri, D.K.: On a class of error correcting binary group codes. Information and Control 3(1), 68-79 (1960)
4. Cardoso, J.S., da Costa, J.F.P.: Learning to classify ordinal data: The data replication method. Journal of Machine Learning Research 8, 1393-1429 (2007)
5. Crammer, K., Singer, Y.: On the learnability and design of output codes for multiclass problems. Machine Learning 47(2-3), 201-233 (2002)
6. Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2, 263-286 (1995)
7. Escalera, S., Pujol, O., Radeva, P.: Decoding of ternary error correcting output codes. In: Martí́nez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds.) CIARP 2006. LNCS, vol.4225, pp. 753-763. Springer, Heidelberg (2006)
8. Friedman, J.H.: Another approach to polychotomous classification. Technical report, Department of Statistics, Stanford University, Stanford, CA (1996)
9. F̈urnkranz, J.: Round robin classification. Journal of Machine Learning Research 2, 721-747 (2002)
10. Ḧullermeier, E., F̈urnkranz, J., Cheng, W., Brinker, K.: Label ranking by learning pairwise p. Artificial Intelligence 172, 1897-1916 (2008)
11. Macwilliams, F.J., Sloane, N.J.A.: The Theory of Error-Correcting Codes. North-Holland Mathematical Library. North Holland, Amsterdam (1983)
12. Park, S.-H., F̈urnkranz, J.: Efficient pairwise classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenic, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol.4701, pp. 658-665. Springer, Heidelberg (2007)
13. Park, S.-H., F̈urnkranz, J.: Efficient decoding of ternary error-correcting output codes for multiclass classification. Technical Report TUD-KE-2009-2101, TU Darmstadt, Knowledge Engineering Group (2009)
14. Pimenta, E., Gama, J., de Leon Ferreira de Carvalho, A.C.P.: The dimension of ECOCs for multiclass classification problems. International Journal on Artificial Intelligence Tools 17(3), 433-447 (2008)
15. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
16. Wu, T.-F., Lin, C.-J.,Weng, R.C.: Probability estimates for multi-class classification by pairwise coupling. Journal of Machine Learning Research 5, 975-1005 (2004)