Ant-based approach to the knowledge fusion problem

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

Volumn 4150 LNCS, Issue , 2006, Pages 84-95

  Martens, David ^a   De Backer, Manu ^a   Haesen, Raf ^a   Baesens, Bart ^a,b   Mues, Christophe ^a,b   Vanthienen, Jan ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; GLOBAL OPTIMIZATION; PATTERN RECOGNITION; PROBLEM SOLVING;

COUNTER INTUITIVE PATTERNS; DOMAIN KNOWLEDGE; EXTRACT CLASSIFICATION MODELS; KNOWLEDGE FUSION PROBLEM;

KNOWLEDGE REPRESENTATION;

EID: 33751373430     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11839088_8     Document Type: Conference Paper

References (14)

1. Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann (2005)
2. Feelders, A., Pardoel, M.: Pruning for monotone classification trees. In: Advanced in intelligent data analysis V. LNCS 2810, Springer (2003) 1-12
3. Altendorf, E., Restificar, E., Dietterich, T.: Learning from sparse data by exploiting monotonicity constraints. In: Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, Edinburgh, Scotland (2005) 18-26
4. Ben-David, A.: Monotonicity maintenance in information-theoretic machine learning algorithms. Machine Learning 19(1) (1995) 29-43
5. Velikova, M., Daniels, H., Feelders, A.: Solving partially monotone problems with neural networks. In: Proceedings of the International Conference on Neural Networks, Vienna, Austria (2006)
6. Pazzani, M., Mani, S., Shankle, W.: Acceptance by medical experts of rules generated by machine learning. Methods of Information in Medicine 40(5) (2001) 380-385
7. Velikova, M., Daniels, H.: Decision trees for monotone price models. Computational Management Science 1(3-4) (2004) 231-244
8. Daniels, H., Velikova, M.: Derivation of monotone decision models from non-monotone data. Discussion Paper 30, Tilburg University, Center for Economic Research (2003)
9. Martens, D., De Backer, M., Haesen, R., Baesens, B.: A MAX-MIN ant system working towards comprehensible classifiers. (Under Review)
10. Martens, D., De Backer, M., Haesen, R., Baesens, B., Holvoet, T.: Ants constructing rule-based classifiers. In: Swarm Intelligence and Data Mining. Studies in Computational Intelligence. Springer (2006)
11. Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation 6(4) (2002) 321-332
12. Stützte, T., Hoos, H.H.: MAX-MIN ant system. Future Generation Computer Systems 16(8) (2000) 889-914
13. Vanthienen, J., Mues, C., Aerts, A.: An illustration of verification and validation in the modelling phase of KBS development. Data and Knowledge Engineering 27(3) (1998) 337-352
14. Hettich, S., Bay, S.D.: The uci kdd archive [http://kdd.ics.uci.edu] (1996)