A Comparison of Logistic Regression, k-Nearest Neighbor, and Decision Tree Induction for Campaign Management

10th Americas Conference on Information Systems, AMCIS 2004

Volumn , Issue , 2004, Pages 1918-1925

  Bichler, Martin ^a   Kiss, Christine ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Campaign management; classification; data mining; direct mail marketing

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMMERCE; DECISION TREES; LEARNING ALGORITHMS; LEARNING SYSTEMS; MARKETING; NEAREST NEIGHBOR SEARCH;

BUSINESS DOMAIN; CAMPAIGN MANAGEMENTS; DATA MINING PROBLEMS; DATA SET; DECISION TREE INDUCTION; DIRECT MAIL MARKETING; DIRECT MARKETING; LOGISTICS REGRESSIONS; MACHINE LEARNING METHODS; MACHINE LEARNING TECHNIQUES;

DATA MINING;

EID: 85139002585     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Apte, C., Liu, B., Pednault, E., and Smyth, P. (2002) Business Applications of Data Mining, CACM (45:8) 2002.
2. Dasarathy, B.V. (1991) Nearest neighbor (NN) Norms: NN pattern classification techniques, IEEE.
3. Friedman, J.H. (1984) Smart user's guide, Department of Statistics, Stanford University, Palo Alto.
4. Gersten, W., Wirth, R., and Arndt, D. (2000) Predictive Modeling in Automotive Direct Marketing: Tools, Experiences and Open Issues," KDD 2000, ACM, Boston, MA, USA, 2000.
5. Gilpin, A.E., and Ohlsen, R.A. (1990) Predicting 1-Year Outcome following Acute Myocardial Infarction: Physicians versus Computers, Computers and Biomedical Research (23) 1990, pp 46-63.
6. Harrell, F.E., and Lee, K.L. (1985) A comparison of the discrimination of discriminant analysis and logistic regression under multivariate normality, in: Biostatistics: Statistics in Biomedical, Public Health, and Environmental Sciences, P.K. Sen (ed.), North Holland, Amsterdam, 1985, pp. 333-343.
7. Long, W., Griffith, J.L., Selker, H.P., and Agostino, R.B. (1993) A Comparison of Logistic Regression to Decision-Tree Induction in a Medical Domain, Computers in Biomedical Research (26) 1993, pp 74-97.
8. Menard, S. (1995) Applied Logistic Regression Analysis Sage Publications, 1995.
9. Michie, D., Spiegelhalter, D.J., and Taylor, C.C. (1994) Machine Learning, Neural and Statistical Classification Ellis Horwood, New York, 1994.
10. Mingers, J. (1987) Rule Induction with Statistical Data-A Comparison with Multiple Regression, Journal of Operational Research Society (38) 1987, pp 347-351.
11. Mitchell, T.M. (1997) Machine Learning WCB/Mc Graw Hill, Boston, et al., 1997.
12. Parekkat, A. (2003) Operational comparison of Logistic regression, Decision trees & Neural networks in modelling mobile service churn," VIEWS Conference 2003, London, UK, 2003.
13. Piatetsky-Shapiro, G., and Masand, B. (1999) Estimating Campaign Benefits and Modeling Lift," KDD 1999, ACM, San Diego, CA, USA, 1999.
14. Quinlan, J.R. (1986) Induction of Decision Trees, Machine Learning (1) 1986, pp 81-106.
15. Rosset, S. (1999) Ranking-Methods for Flexible Evaluation and Efficient Comparison of Two-Class Models, in: Statistics, Stanford University, Palo Alto.
16. Rosset, S., Neumann, E., Eick, U., Vatnik, N., and Idan, I. (2001) Evaluation of Prediction Models for Marketing Campaigns," KDD 01, ACM, San Francisco, CA, USA, 2001.
17. Witten, I.H., and Frank, E. (2001) Data Mining Carl Hanser, München, Wien, 2001.
18. Wolpert, D. (1996) The Existence of a Priori Distinctions between Learning Algorithms, Neural Computation (8) 1996, pp 1391-1420.