Journal
12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012)
Volume -, Issue -, Pages 187-193Publisher
IEEE
DOI: 10.1109/ICDMW.2012.18
Keywords
Weighted Self-Organizing Map; instance-varying cost; cost-sensitive classification; cost-sensitive clustering
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This paper presents a Weighted Self-Organizing Map (WSOM). The WSOM combines the advantages of the standard SOM paradigm with learning that accounts for instance-varying importance. While the learning of the classical batch SOM weights data by a neighborhood function, we augment it with a user-specified instance-specific importance weight for cost-sensitive classification. By focusing on instance-specific importance to the learning of a SOM, we take a perspective that goes beyond the common approach of incorporating a cost matrix into the objective function of a classifier. When setting the weight to be the importance of an instance for forming clusters, the WSOM may also be seen as an alternative for cost-sensitive unsupervised clustering. We compare the WSOM with a classical SOM and logit analysis in financial crisis prediction. The performance of the WSOM in the financial setting is confirmed by superior cost-sensitive classification performance.
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