Autocorrelation and linkage cause bias in evaluation of relational learners

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

Volumn 2583, Issue , 2003, Pages 101-116

  Jensen, David ^a   Neville, Jennifer ^a  

^a Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOCORRELATION;

INTERNET MOVIE DATABASE; MODEL ACCURACY; QUANTITATIVE MEASURES; RELATIONAL DATA; SAMPLING PROCEDURES; SYNTHETIC DATA; TEST SETS;

INDUCTIVE LOGIC PROGRAMMING (ILP);

EID: 7044231002     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36468-4_7     Document Type: Conference Paper

References (19)

1. Cortes, C., D. Pregibon, and C. Volinsky (2001). Communities of Interest. Proceedings Intelligent Data Analysis 2001.
2. Craven, M., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T., Nigam, K., & Slattery, S. (1998). Learning to extract symbolic knowledge from the World Wide Web. Proceedings of the Fifteenth National Conference on Artificial Intellligence (pp. 509-516). Menlo Park: AAAI Press.
3. Cressie, N. (1993). Statistics for Spatial Data. Wiley.
4. Flach, P. and N. Lachiche (1999). 1BC: a first-order Bayesian classifier. ILP'99. 92-103. Springer.
5. Getoor, L., N. Friedman, D. Koller, and A. Pfeffer (1999). Learning probabilistic relational models. In IJCAI'99. 1300-1309.
6. Holte, R., Acker, L., & Porter, B. (1989). Concept learning and the accuracy of small disjuncts. Proceedings of the 11th International Joint Conference on Artificial Intelligence (pp. 813- 818). Detroit: Morgan Kaufmann.
7. Jensen, D. and P. Cohen (2000). Multiple comparisons in induction algorithms. Machine Learning 38:309-338.
8. Jensen, D. and J. Neville (2001). Correlation and sampling in relational data mining. Proceedings of the 33rd Symposium on the Interface of Computing Science and Statistics.
9. Jensen, D. and J. Neville (2002). Linkage and autocorrelation cause bias in relational feature selection. Machine Learning: Proceedings of the Nineteenth International Conference. Morgan Kaufmann.
10. John, G., R. Kohavi, and K. Pfleger (1994). Irrelevant features and the subset selection problem. ICML'94. 121-129.
11. Kleinberg, J. (1999). Authoritative sources in a hyper-linked environment. Journal of the ACM 46:604-632
12. Lipton, R., Naughton, J., Schneider, D., & Seshadri, S. (1993). Efficient sampling strategies for relational database operations. Theoretical Computer Science, 116, 195-226.
13. Muggleton, S. (Ed) (1992). Inductive Logic Programming. Academic Press
14. Muggleton, S. (2000). Learning Stochastic Logic Programs. AAAI Workshop on Learning Statistical Models from Relational Data, 36-41.
15. Noreen, E. (1989). Computer Intensive Methods for Testing Hypotheses. Wiley.
16. Neville, J. and D. Jensen (2000). Iterative Classification in Relational Data. AAAI Workshop on Learning Statistical Models from Relational Data, 42-49.
17. Quinlan, J. R. (1990), Learning logical definitions from relations. Machine Learning 5:239-266.
18. Sachs, L. (1982). Applied Statistics. Springer-Verlag.
19. Slattery, S. & Mitchell, T. (2000). Discovering test set regularities in relational domains. Proceedings of the 17th International Conference on Machine Learning. San Francisco: Morgan Kaufmann.