Efficient data mining by active learning

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

Volumn 2281, Issue , 2002, Pages 258-267

  Mamitsuka, Hiroshi ^a   Abe, Naoki ^b  

^a NEC CORPORATION   (Japan)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS;

COMPUTATION TIME; DATA MINING AND KNOWLEDGE DISCOVERY; DATA MINING APPLICATIONS; INDUCTIVE ALGORITHMS; PREDICTIVE ACCURACY; REPRESENTATIVE SAMPLING; SELECTIVE SAMPLING; UNCERTAINTY SAMPLINGS;

DATA MINING;

EID: 84864043622     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45884-0_17     Document Type: Article

References (15)

1. N. Abe and H. Mamitsuka. Query Learning Strategies Using Boosting and Bagging Proceedings of Fifteenth International Conference on Machine Learning, 1-9, 1998.
2. R. Agrawal and T. Imielinski and A. Swami. Database Mining: A Performance Perspective IEEE Transactions on Knowledge and Data Engineering, 5(6):914-925, 1993.
3. L. Breiman. Bagging Predictors Machine Learning 24:123-140, 1996.
4. L. Breiman. Pasting Small Votes for Classification in Large Databases and on-line Machine Learning 36:85-103, 1999.
5. J. Catlett. Megainduction: Atest flight Proceedings of Eighth International Workshop on Machine Learning, 596-599, 1991.
6. Y. Freund and R. Schapire. Adecision-theoretic generalization of on-line learning and an application to boosting Journal of Computer and System Sciences 55(1), 119-139, 1997.
7. J. Furnkranz Integrative windowing Journal of Artificial Intelligence Research 8:129-164, 1998.
8. J. Gehrke and V. Ganti and R. Ramakrishnan and W-Y. Loh BOAT - Optimistic Decision Tree Construction Proceedings of the ACM SIGMOD International Conference on Management of Data, 169-180, 1999.
9. D. Michie and D. Spiegelhalter and C. Taylor (Editors). Machine Learning, Neural and Statistical Classification, Ellis Horwood, London, 1994.
10. F. Provost and V. Kolluri. ASurv ey of Methods for Scaling Up Inductive Algorithms Knowledge Discovery and Data Mining 3(2):131-169, 1999.
11. J. R. Quinlan. Learning efficient classification procedures and their applications to chess endgames Machine Learning: An artificial intelligence approach, R. S. Michalski and J. G. Carbonell and T. M. Mitchell (Editors), San Francisco, Morgan Kaufmann, 1983.
12. J. R. Quinlan C4.5: Programs for Machine Learning, San Francisco, Morgan Kaufmann, 1993.
13. R. Rastogi and K. Shim Public: ADecision Tree Classifier that integrates building and pruning Proceedings of 24th International Conference on Very Large Data Bases, New York, Morgan Kaufmann, 404-415, 1998.
14. H. S. Seung and M. Opper and H. Sompolinsky. Query by committee Proceedings of 5th Annual Workshop on Computational Learning Theory, 287-294, New York, ACM Press, 1992.
15. S. M. Weiss and N. Indurkhya Predictive Data Mining, Morgan Kaufmann, San Francisco, 1998.