Sequential Automatic Search of a Subset of Classifiers in Multiclass Learning

Francesco Mola () and Claudio Conversano ()
Additional contact information
Francesco Mola: University of Cagliari, Department of Economics
Claudio Conversano: University of Cagliari, Department of Economics

A chapter in COMPSTAT 2008, 2008, pp 291-302 from Springer

Abstract: Abstract A method called Sequential Automatic Search of a Subset of Classifiers is hereby introduced to deal with classification problems requiring decisions among a wide set of competing classes. It utilizes classifiers in a sequential way by restricting the number of competing classes while maintaining the presence of the true (class) outcome in the candidate set of classes. Some features of the method are discussed, namely: a cross-validation-based criteria to select the best classifier in each iteration of the algorithm, the resulting classification model and the possibility of choosing between an heuristic or probabilistic criteria to predict test set observations. Furthermore, the possibility to cast the whole method in the framework of unsupervised learning is also investigated. Advantages of the method are illustrated analyzing data from a letter recognition experiment.

Keywords: classification; subset selection; decision tree; cross-validation; rule (search for similar items in EconPapers)
Date: 2008
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2084-3_24

Ordering information: This item can be ordered from
http://www.springer.com/9783790820843

DOI: 10.1007/978-3-7908-2084-3_24

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().