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2012 IEEE 12th International Conference on Data Mining Workshops
A Weighted SOM for Classifying Data with Instance-Varying Importance
Brussels, Belgium Belgium
December 10-December 10
ISBN: 978-1-4673-5164-5
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.
Index Terms:
Training,Standards,Vectors,Computational modeling,Prediction algorithms,Hidden Markov models,Loss measurement,cost-sensitive clustering,Weighted Self-Organizing Map,instance-varying cost,cost-sensitive classification
Citation:
Peter Sarlin, "A Weighted SOM for Classifying Data with Instance-Varying Importance," icdmw, pp.187-193, 2012 IEEE 12th International Conference on Data Mining Workshops, 2012
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