Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/6527
Title: | Comparing complete and partial classification for identifying latently dissatisfied customers | Authors: | BRIJS, Tom SWINNEN, Gilbert VANHOOF, Koen WETS, Geert |
Issue Date: | 2000 | Publisher: | SPRINGER-VERLAG BERLIN | Source: | MACHINE LEARNING: ECML 2000. p. 88-95 | Series/Report: | LECTURE NOTES IN ARTIFICIAL INTELLIGENCE | Series/Report no.: | 1810 | Abstract: | This paper evaluates complete versus partial classification for the problem of identifying latently dissatisfied customers. Briefly, latently dissatisfied customers are defined as customers reporting overall satisfaction but who possess typical characteristics of dissatisfied customers. Unfortunately, identifying latenty dissatisfied customers, based on patterns of dissatisfaction, is difficult since in customer satisfaction surveys, typically only a small minority of customers reports to be overall dissatisfied and this is exactly the group we want to focus learning on. Therefore, it has been claimed that since traditional (complete) classification techniques have difficulties dealing with highly skewed class distributions, the adaption of partial classification techniques could be more appropriate. We evaluate three different complete and partial classification techniques and compare their performance on a ROC convex hull graph. Results on real world data show that, under the circumstances described abobe, partial classification is indeed a serious competitor for complete classification. Moreover, external validation on holdout data shows that partial classification is able to identify latently dissatisfied customers correctly. | Document URI: | http://hdl.handle.net/1942/6527 | ISI #: | 000166853300010 | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2002 |
Appears in Collections: | Research publications |
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.