An evolutionary algorithm for cost-sensitive decision rule learning

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

Volumn 2167, Issue , 2001, Pages 288-299

  Kwedlo, Wojciech ^a   Krȩtowski, Marek ^a  

^a BIALYSTOK UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ERRORS; GENETIC ALGORITHMS; MACHINE LEARNING;

CLASSIFICATION ERRORS; CLASSIFICATION LEARNING; COST-SENSITIVE LEARNING; FITNESS FUNCTIONS; MISCLASSIFICATION COSTS; MULTIVARIATE DISCRETIZATION; REAL-LIFE APPLICATIONS; REMAINING COMPONENT;

LEARNING ALGORITHMS;

EID: 84948156363     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44795-4_25     Document Type: Conference Paper

References (21)

1. Blake, C., Keogh, E., Merz, C. J.: UCI repository of machine learning databases, [http://www.ics.uci.edu/_mlearn/MLRepository.html]. Irvine, CA: University of California, Dept. of Computer Science (1998).
2. Bradford, J. P., Kunz, C., Kohavi, R., Brunk, C., Brodley, C. E.: Pruning decision trees with misclassification costs. In Proc. of the Tenth European Conf. on Machine Learning. Springer Verlag (1998) 131-136.
3. Breiman, L., Friedman, R. A., Olshen, R., Stone, C. J.: Classification and Regression Trees. Wadsworth (1984).
4. Bruha, I., Quality of decision rules: Definitions and classification schemes for multiple rules. In: Nakhaeizadeh, G., Taylor, C. C., (eds.) Machine Learning and Statistics. The Interface. Wiley-Interscience (1997) 107-131.
5. De Jong, K., Spears, W. M., Gordon, D. F.: Using genetic algorithm for concept learning. Machine Learning 13 (1993) 168-182.
6. Domingos, P.: MetaCost: A general method for making classifiers cost-sensitive. In Proc. of Int. Conf. on Knowledge Discovery and Data Mining, KDD’99. ACM Press (1999) 155-164.
7. Fayyad, U. M., Irani, K. B.: Multi-interval discretization of continuous-valued attributes for classification learning. In Proc. of IJCAI’93. Morgan Kaufmann (1993) 1022-1027.
8. Fawcett, T., Provost, F. J., Adaptive fraud detection, Data Mining and Knowledge Discovery 1 (1997)
9. Giordana, A., Neri, F.: Search-intensive concept induction. Evolutionary Computation 3(4) (1995) 375-416.
10. Janikow, C.: A knowledge intensive genetic algorithm for supervised learning. Machine Learning 13 (1993) 192-228.
11. Knoll, U., Nakhaeizadeh, G., Tausend, B.: Cost-sensitive pruning of decision trees. In Proc. of the 8th European Conf. on Machine Learning. Springer LNCS 784 (1994) 383-386.
12. Kwedlo, W., Krȩtowski, M.: An evolutionary algorithm using multivariate discretization for decision rule induction. In Principles of Data Mining and Knowledge Discovery. 3rd European Conference PKDD’99. Springer LNCS 1704 (1999) 392-397.
13. Kwedlo, W., Krȩtowski, M.: Discovery of decision rules from databases: an evolutionary approach. In Principles of Data Mining and Knowledge Discovery. 2nd European Symposium PKDD’98. Springer LNCS 1510 (1998) 370-378.
14. Margineantu, D. D., Dietterich, T. G.: Bootstrap methods for the cost-sensitive evaluation of classifiers, In Proc. 17th Int. Conf. on Machine Learning ICML’2000, Morgan Kaufmann (2000) 583-590.
15. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. 3rd edn. Springer (1996).
16. Michie, D., Spiegelhalter, D. J., Taylor, C. C.: Machine Learning, Neural and Statistical Classification. Ellis Horwood Ltd. (1994) [also available at http://www.amsta.leeds.ac.uk/_charles/statlog/index.html].
17. Pazzani, M., Merz, C., Murphy, P., Ali, K., Hume, T., Brunk, C.: Reducing misclassification costs. In Proc. of Int. Conf. on Machine Learning, ICML’94. Morgan Kaufmann (1994) 217-225.
18. Quinlan, J. R.: C4. 5: Programs for Machine Learning. Morgan Kaufmann (1993).
19. Ting, K. M.: Inducing cost-sensitive trees via instance weighting. In Principles of Data Mining and Knowledge Discovery. 2nd European Symposium PKDD’98. Springer LNCS 1510 (1998) 139-147.
20. Turney, P.: Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Artificial Intelligence Research 2 (1995) 369-409.
21. Turney, P.: Types of cost in inductive concept learning. In Proc. of ICML’2000 Workshop on Cost-Sensitive Learning. Stanford, CA (2000).