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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
WETS, Geert
Issue Date: 2000
Citation: MACHINE LEARNING: ECML 2000. p. 88-95
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.
URI: http://hdl.handle.net/1942/6527
ISI #: 000166853300010
ISSN: 0302-9743
Category: A1
Type: Journal Contribution
Validation: ecoom, 2002
Appears in Collections: Research publications

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