C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
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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 adoption 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.