Learning classification rules for telecom customer call data under concept drift

Soft Computing

Volumn 8, Issue 2, 2003, Pages 102-108

  Black, M ^a   Hickey, R ^a  

^a UNIVERSITY OF ULSTER   (United Kingdom)

Author keywords

Adaptive learning; Concept drift; Decision trees; User profiling

Indexed keywords

EID: 24644449205     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-002-0250-2     Document Type: Article

References (17)

1. Black M, Hickey RJ (1999) Maintaining the performance of a learned classifier under concept drift, Intelligent Data Analysis 3: 453-474
2. Hickey RJ, Black M (2001) Refined time stamps for concept drift detection during mining for classification rules. In: Roddick K, Hornsby JF (Eds) Spatio-Temporal Data Mining (TSDM 2000). Lecture Notes in Artificial Intelligence 2007. Springer, Berlin Heidelberg New York, pp. 20-30
3. Hickey RJ (1996) Noise modelling and evaluating learning from examples, Artificial Intelligence 82: 157-179
4. Kelly MG, Hand DJ, Adams NM (1999) The impact of changing populations on classifier performance. In: Chaudhuri S, Madigan D (Eds) Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA: ACM Press, pp. 367-371
5. Klenner M, Hahn U (1994) Concept versioning: A methodology for tracking evolutionary concept drift in dynamic concept systems. In: Cohn AG (Eds) Proceedings of Eleventh European Conference on Artificial Intelligence. Amsterdam, Nertherlands, Wiley, Chichester, England, pp. 473-477
6. Schlimmer JC, Granger RH (1986) Incremental learning from noisy data, Machine Learning 1: 317-354
7. Hembold DP, Long PM (1994) Tracking drifting concepts by minimising disagreements, Machine Learning 14: 27-45
8. Hulten G, Spencer L, Domingos P (2001) Mining time-changing data streams. In: Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining. San Francisco, CA: ACM Press, pp. 97-106
9. Widmer G (1997) Tracking changes through meta-learning, Machine Learning 27: 259-286
10. Widmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts, Machine Learning 23: 69-101
11. Chakrabarti S, Sarawagi S, Dom B (1998) Mining surprising patterns using temporal description length. In: Gupta A, Shmueli O, Widom J (Eds) Proceedings of the Twenty-Fourth International Conference on Very Large databases. Morgan Kaufmann, San Mateo, California, pp. 606-661
12. Chen X, Petrounias I (1999) Mining temporal features in association rules. In: Zytkow J, Rauch J (Eds) Proceedings of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases. Lecture Notes in Artificial Intelligence, Vol. 1704. Springer, Berlin Heidelberg New York, pp. 295-300
13. Quinlan JR (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, California
14. Quinlan JR (1998) See5. www.rulequest.com/
15. Utgoff PE (1997) Decision tree induction based on efficient tree restructuring, Machine Learning 29: 5-44
16. Clark P, Boswell R (1991) Rule induction with CN2: some recent improvements. In: Kodratof (Eds) Proceedings of the European Workshop on Learning (EWSL-91). Lecture Notes in Artificial Intelligence. Springer, Berlin Heidelberg New York, pp. 151-163
17. Bratko I (2001) Prolog programming for artificial intelligence, 3rd edn. Addison-Wesley, Wokingham, England