Connectionist construction of prototypes from decision trees for graph classification

Intelligent Data Analysis

Volumn 7, Issue 2, 2003, Pages 125-140

  Geibel, Peter ^a   Schädler, Kristina ^b   Wysotzki, Fritz ^a  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^b Pace Aerospace Engineering and Information Technology   (Germany)

Author keywords

decision trees; graphs; hopfield nets; inductive logic programming; machine learning

Indexed keywords

DECISION TREES; HOPFIELD NEURAL NETWORKS; INDUCTIVE LOGIC PROGRAMMING (ILP); LEARNING SYSTEMS; PATTERN MATCHING;

CLASSIFICATION TASKS; CONNECTIONIST APPROACH; DECISION-TREE ALGORITHM; GENERALIZED GRAPHS; GRAPH CLASSIFICATION; GRAPHS; HOPFIELD NETS; RELATIONAL LEARNING;

TREES (MATHEMATICS);

EID: 79551564872     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2003-7204     Document Type: Article

References (29)

1. G. Bisson. Conceptual clustering in a first order logic representation, in: Proc. of the 10th ECAI, B. Neumann, ed., John Wiley & Sons, 1992, pp. 458-462.
2. H. Blockeel, L. DeRaedt and J. Ramon, Top-down induction of clustering trees, in: Proceedings of the 15th International Conference on Machine Learning, J. Shavlik, ed., 1998, pp. 55-63.
3. J. Bruck and J.W. Goodman, On the power of neural networks for solving hard problems, Journal of Complexity 6 (1990), 129-135.
4. L.I. Burke and J.P. Ignizio, Neural networks and operations research: An overview, Computers Ops. Res. 19(3/4) (1992), 179-189.
5. B. Dolsak and S. Muggleton, The application of Inductive Logic Programming to finite element mesh design, in: Inductive Logic Programming, S. Muggleton, ed., Academic Press, London, 1992.
6. W. Emde and D.Wettschereck, Relational instance based learning, in: Proc. 13th ICML, L. Saitta, ed., Morgan Kaufmann Publishers, 1996, pp. 122-130.
7. F. Esposito, A. Laterza, D. Malerba and G. Semerano, Refinement of datalog programs, in: ICML '96 Workshop on "Datamining with Inductive Logic Programming, 1996, pp. 73-94.
8. J.A. Feldman, M.A. Fanty, N.H. Goddard and K.J. Lynne, Computing with structured connectionist networks, CACM 31(2) (February 1988), 170-187.
9. P. Geibel, Graph based induction of spatial relations in mental models, in: Learning Spatial Relations with CAL5 and TRITOP, number 98-7 in Rote Reihe, P. Geibel, C. Gips, S. Wiebrock and F. Wysotzki, eds, Fachbereich Informatik, Technische Universiẗat Berlin, 1998, pp. 25-51. URL http://www.cs.tu- berlin.de/? geibel/publications.html.
10. P. Geibel, Induktive Konstruktion von merkmalsbasierten und logischen Klassifikatoren f̈ur relationale Strukturen (Inductive Construction of Feature Based and Logical Classifiers for Relational Structures), PhD thesis, TU Berlin, 1999, in German.
11. P. Geibel and F.Wysotzki, Learning relational concepts with decision trees, in: Machine Learning: Proc. of the 13th Int. Conf., L. Saitta, ed., Morgan Kaufmann Publishers, San Fransisco, CA, 1996.
12. P. Geibel and F. Wysotzki, A logical framework for graph theoretic decision tree learning, in: Proc. ILP 97, N. Lavrac and S. Dzeroski, eds, Springer, 1997.
13. D. Haussler, Learning Conjunctive Concepts in Structural Domains, Machine Learning 4 (1989), 7-40.
14. N. Helft, Inductive generalization: A logical framework, in: Proceedings of the Second Working Session on Learning, 1987, pp. 149-157.
15. J.-U. Kietz, A comparative study of structural most specific generalisations used in machine learning, in: Proc. ILP-93, Ljubljana, Slovenia, J. Stefan Institute Tech. Rep. IJS-DP-6707, 1993, pp. 149-164.
16. M. Manago, Knowledge intensive induction, in: Proceedings of the 6th International Workshop on Machine Learning, A.M. Segre, ed., Ithaca, Morgan Kaufmann, 1989, pp. 151-155.
17. S. Muggleton, Inverse entailment and Progol, New Generation Computing, Special issue on Inductive Logic Programming 13(3-4) (1995), 245-286.
18. G.D. Plotkin, A note on inductive generalization, in: Machine Intelligence, Edinburgh University Press, 1969, pp. 153-164.
19. J.R. Quinlan, Learning Logical Definitions from Relations, Machine Learning 5 (1990), 239-266.
20. J.R. Quinlan, Induction of Decision Trees, Machine Learning 1(1) (1986), 82-106.
21. L. De Raedt and H. Blockeel, Using logical decision trees for clustering, in: Proc. of the 7th Int. WS on ILP, volume 1297 of LNAI, N. Lavra?c and S. D?zeroski, eds, Springer, September 17-20, 1997, pp. 133-140.
22. K. Scḧadler and F. Wysotzki, A connectionist approach to distance-based analysis of relational data, in: Proc. of the IDA-97, X. Liu, P. Cohen and M. Berthold, eds, Springer, 1997, pp. 137-148.
23. K. Scḧadler and F. Wysotzki, Theoretical foundations of a special neural net approach for graphmatching, Technical Report 96-26, TU Berlin, CS Dept., 1996.
24. K. Scḧadler and F.Wysotzki, Comparing structures using a hopfield-style neural network, Applied Intelligence-special issue on Neural Networks and Structured Knowledg 11(1) (1999), 5-30.
25. A. Srinivasan and R.D. King, Using Inductive Logic Programming to construct Structure-Activity Relationships, in: Predictive toxicology of chemicals: experiences and impact of AI tools (Papers from the 1999 AAAI Spring Symposium), G.C. Gini and A.R. Katrizsky, eds, AAAI Press, Menlo Park, CA, 1999, pp. 64-73.
26. A. Srinivasan, S. Muggleton, R. King and M. Sternberg, Mutagenesis: ILP experiments in a non-determinate biological domain, in: Proc. of the Fourth Intl. WS on ILP, number 237 in GMD-Studien, S. Wrobel, ed., 1994, pp. 217-232.
27. A. Srinivasas, R.D. Kind, S.H. Muggleton and M.J.E. Sternberg, Carcinogenesis predictions using ILP, in: Proc. ILP-97, number 1297 in LNAI, N. Lavrac and S. Dzeroski, eds, Springer-Verlag, 1997, pp. 273-287.
28. S.A. Vere, Induction of Concepts in the Predicate Calculus, in: Proc. of the Fourth Intl. Joint Conf. on AI, (Vol. 1), 1975, pp. 281-287.
29. F.Wysotzki, Artificial Intelligence and Artificial Neural Nets, in: Proc. 1st WS on AI, TU Berlin and Jiao Tong University Shanghai, Shanghai, September 1990.