@article{keyhere,
	title = {A Cost-Sensitive Paradigm for Multiclass to Binary Decomposition Schemes},
	author = {Claudio Marrocco and Francesco Tortorella},
	journal = {Structural, Syntactic, and Statistical Pattern Recognition},
	pages = {753--761},
	url = {http://www.springerlink.com/content/5fdg88yxqvwale7j},
	year = {2004},
	biburl = {http://www.bibsonomy.org/bibtex/2a234beda6a9a042041c89b21c8291eb0/hotho},
	description = {SpringerLink - Book Chapter},
	abstract = {An established technique to face a multiclass categorization problem is to reduce it into a set of two-class problems. To this aim, the main decomposition schemes employed are one vs. one, one vs. all and Error Correcting Output Coding. A point not yet considered in the research is how to apply these methods to a cost-sensitive classification that represents a significant aspect in many real problems. In this paper we propose a novel method which, starting from the cost matrix for the multi-class problem and from the code matrix employed, extracts a cost matrix for each of the binary subproblems induced by the coding matrix. In this way, it is possible to tune the single two-class classifier according to the cost matrix obtained and achieve an output from all the dichotomizers which takes into account the requirements of the original multi-class cost matrix. To evaluate the effectiveness of the method, a large number of tests has been performed on real data sets. The experiments results have shown a significant improvement in terms of classification cost, specially when using the ECOC scheme.
ER  -},
	keywords = {class classifier multi svm }
}