The Power of Sampling and Stacking for the PaKDD-2007 Cross-Selling Problem

Paulo J.L. Adeodato, Germano C. Vasconcelos, Adrian L. Arnaud, Rodrigo C.L.V. Cunha, Domingos S.M.P. Monteiro and Rosalvo F.O. Neto
Additional contact information
Paulo J.L. Adeodato: NeuroTech Ltd. and Federal University of Pernambuco, Brazil
Germano C. Vasconcelos: NeuroTech Ltd. and Federal University of Pernambuco, Brazil
Adrian L. Arnaud: NeuroTech Ltd. and Federal University of Pernambuco, Brazil
Rodrigo C.L.V. Cunha: NeuroTech Ltd. and Federal University of Pernambuco, Brazil
Domingos S.M.P. Monteiro: NeuroTech Ltd. and Federal University of Pernambuco, Brazil
Rosalvo F.O. Neto: NeuroTech Ltd. and Federal University of Pernambuco, Brazil

International Journal of Data Warehousing and Mining (IJDWM), 2008, vol. 4, issue 2, 22-31

Abstract: This article presents an efficient solution for the PAKDD-2007 Competition cross-selling problem. The solution is based on a thorough approach which involves the creation of new input variables, efficient data preparation and transformation, adequate data sampling strategy and a combination of two of the most robust modeling techniques. Due to the complexity imposed by the very small amount of examples in the target class, the approach for model robustness was to produce the median score of the 11 models developed with an adapted version of the 11-fold cross-validation process and the use of a combination of two robust techniques via stacking, the MLP neural network and the n-tuple classifier. Despite the problem complexity, the performance on the prediction data set (unlabeled samples), measured through KS2 and ROC curves was shown to be very effective and finished as the first runner-up solution of the competition.

Date: 2008
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 4018/jdwm.2008040104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jdwm00:v:4:y:2008:i:2:p:22-31

Access Statistics for this article

International Journal of Data Warehousing and Mining (IJDWM) is currently edited by Eric Pardede

More articles in International Journal of Data Warehousing and Mining (IJDWM) from IGI Global
Bibliographic data for series maintained by Journal Editor ().