First order random forests with complex aggregates

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3194, Issue , 2004, Pages 323-340

  Vens, Celine ^a   Van Assche, Anneleen ^a   Blockeel, Hendrik ^a   Džeroski, Sašo ^b  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b JO EF STEFAN INSTITUTE   (Slovenia)

Author keywords

Aggregation; Decision Tree Learning; Random forests

Indexed keywords

AGGLOMERATION; AGGREGATES; FUNCTIONS; LEARNING SYSTEMS; LOGIC PROGRAMMING; RELATIONAL DATABASE SYSTEMS; TREES (MATHEMATICS); DECISION TREES;

DECISION TREE LEARNING; DECISION TREES; INDUCTIVE LOGIC PROGRAMMING (ILP); RANDOM FORESTS; AGGREGATE FUNCTION; BAGGING METHOD; COMPLEX AGGREGATES; FEATURE SETS; FEATURE SPACE; FIRST ORDER THEORIES;

DECISION THEORY; INDUCTIVE LOGIC PROGRAMMING (ILP);

EID: 22944433186     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-30109-7_24     Document Type: Conference Paper

References (26)

1. P. Berka. Guide to the financial data set. In A. Siebes and P. Berka, editors, The ECML/PKDD 2000 Discovery Challenge, 2000.
2. H. Blockeel and M. Bruynooghe. Aggregation versus selection bias, and relational neural networks. In IJCAI-2003 Workshop on Learning Statistical Models from Relational Data, SRL-2003, Acapulco, Mexico, August 11, 2003, 2003.
3. H. Blockeel and L. De Raedt. Lookahead and discretization in ILP. In Proceedings of the Seventh International Workshop on Inductive Logic Programming, volume 1297 of Lecture Notes in Artificial Intelligence, pages 77-85. Springer-Verlag, 1997.
4. H. Blockeel and L. De Raedt. Top-down induction of first order logical decision trees. Artificial Intelligence, 101(1-2):285-297, June 1998.
5. H. Blockeel, L. Dehaspe, B. Demoen, G. Janssens, J. Ramon, and H. Vandecasteele. Improving the efficiency of inductive logic programming through the use of query packs. Journal of Artificial Intelligence Research, 16:135-166, 2002.
6. L. Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
7. L. Breiman. Out-of-bag estimation. ftp.stat.berkeley.edu/pub/users/ breiman/OOBestimation.ps, 1996.
8. L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
9. L. De Raedt and W. Van Laer. Inductive constraint logic. In K. P. Jantke, T. Shinohara, and T. Zeugmann, editors, Proceedings of the Sixth International Workshop on Algorithmic Learning Theory, volume 997 of Lecture Notes in Artificial Intelligence, pages 80-94. Springer-Verlag, 1995.
10. T. Dietterich. Ensemble methods in machine learning. In Proceedings of the 1th International Workshop on Multiple Classifier Systems, volume 1857 of Lecture Notes in Computer Science, pages 1-15, 2000.
11. 13C NMR spectra with inductive logic programming. Applied Artificial Intelligence, 12(5):363-384, July-August 1998.
12. W. Emde and D. Wettschereck. Relational instance based learning. In Proceedings of the 1995 Workshop of the GI Special Interest Group on Machine Learning, 1995.
13. Y. Freund and R. E. Schapire. Experiments with a new boosting algorithm. In L. Saitta, editor, Proceedings of the Thirteenth International Conference on Machine Learning, pages 148-156. Morgan Kaufmann, 1996.
14. L. Hansen and P. Salamon. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12:993-1001, 1990.
15. D. Jensen, J. Neville, and M. Hay. Avoiding bias when aggregating relational data with degree disparity. In Proceedings of the 20th International Conference on Machine Learning, 2003.
16. A. Knobbe, A. Siebes, and B. Marseille. Involving aggregate functions in multi-relational search. In Principles of Data Mining and Knowledge Discovery, Proceedings of the 6th European Conference, pages 287-298. Springer-Verlag, August 2002.
17. D. Koller. Probabilistic relational models. In Proceedings of the Ninth International Workshop on Inductive Logic Programming, volume 1634 of Lecture Notes in Artificial Intelligence, pages 3-13. Springer-Verlag, 1999.
18. M.-A. Krogel and S. Wrobel. Transformation-based learning using multi-relational aggregation. In Proceedings of the Eleventh International Conference on Inductive Logic Programming, pages 142-155, 2001.
19. N. Lavrač and S. Džeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, 1994.
20. S. Muggleton, editor. Inductive Logic Programming. Academic Press, 1992.
21. S. Muggleton. Inverse entailment and Progol. New Generation Computing, Special issue on Inductive Logic Programming, 13(3-4):245-286, 1995.
22. J. Neville, D. Jensen, L. Friedland, and M. Hay. Learning relational probability trees. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003.
23. C. Perlich and F. Provost. Aggregation-based feature invention and relational concept classes. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 167-176. ACM Press, 2003.
24. J. Quinlan. Learning logical definitions from relations. Machine Learning, 5:239-266, 1990.
25. J. R. Quinlan. C4-5: Programs for Machine Learning. Morgan Kaufmann series in Machine Learning. Morgan Kaufmann, 1993.
26. A. Srinivasan, R. King, and D. Bristol. An assessment of ILP-assisted models for toxicology and the PTE-3 experiment. In Proceedings of the Ninth International Workshop on Inductive Logic Programming, volume 1634 of Lecture Notes in Artificial Intelligence, pages 291-302. Springer-Verlag, 1999.