Constraint based induction of multi-objective regression trees

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

Volumn 3933 LNCS, Issue , 2006, Pages 222-233

  Struyf, Jan ^a   Džeroski, Sašo ^b  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b JO EF STEFAN INSTITUTE   (Slovenia)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRAINT THEORY; DATA STRUCTURES; DECISION THEORY; QUERY LANGUAGES; REGRESSION ANALYSIS;

ACCURACY CONSTRAINTS; DATA SETS; INDUCTIVE DATABASE; USER CONSTRAINTS;

DATABASE SYSTEMS;

EID: 33745775676     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11733492_13     Document Type: Conference Paper

References (17)

1. H. Almuallim. An efficient algorithm for optimal pruning of decision trees. Artificial Intelligence, 83(2):347-362, 1996.
2. H. Blockeel, L. De Raedt, and J. Ramon. Top-down induction of clustering trees. In Proceedings of the 15th International Conference on Machine Learning, pages 55-63, 1998.
3. M. Bohanec and I. Bratko. Trading accuracy for simplicity in decision trees. Machine Learning, 15(3):223-250, 1994.
4. L. Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
5. L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
6. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone. Classification and Regression Trees. Wadsworth, Belmont, 1984.
7. L. De Raedt. A perspective on inductive databases. SIGKDD Explorations, 4(2).-69-77, 2002.
8. D. Demšar, M. Debeljak, C. Lavigne, and S. Džeroski. Modelling pollen dispersal of genetically modified oilseed rape within the field, 2005. Abstract presented at The Annual Meeting of the Ecological Society of America, Montreal, Canada, 7-12 August 2005.
9. D. Demšar, S. Džeroski, P. Henning Krogh, T. Larsen, and J. Struyf. Using multiobjective classification to model communities of soil microarthropods. Ecological Modelling, 2005. To appear.
10. S. Džeroski, D. Demšar, and J. Grbovic. Predicting chemical parameters of river water quality from bioindicator data. Applied Intelligence, 13(1):7-17, 2000.
11. S. Džeroski, N. Colbach, and A. Messean. Analysing the effect of field characteristics on gene flow between oilseed rape varieties and volunteers with regression trees, 2005. Submitted to the The Second International Conference on Co-existence between GM and non-GM based agricultural supply chains (GMCC-05). Montpellier, Prance, 14-15 November 2005.
12. M. Garofalakis, D. Hyun, R. Rastogi, and K. Shim. Building decision trees with constraints. Data Mining and Knowledge Discovery, 7(2):187-214, 2003.
13. T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Communications of the. ACM, 39(11):58-64, 1996.
14. C. Kampichler, S. Džeroski, and R. Wieland. The application of machine learning techniques to the analysis of soil ecological data bases: Relationships between habitat features and collembola community characteristics. Soil Biology and Biochemistry, 32:197-209, 2000.
15. J. R. Quinlan. C4-5: Programs for Machine Learning. Morgan Kaufmann series in Machine Learning. Morgan Kaufmann, 1993.
16. R. S. Sain and P. S. Carmack. Boosting multi-objective regression trees. Computing Science and Statistics, 34:232-241, 2002.
17. I. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005. 2nd Edition.