Pursuing the best ECOC dimension for multiclass problems

• Type: Article in International Conference Proceedings Book
• Date: 2007

Authors

Pimenta, E (Author) Other; João Gama (Author) FEP; Carvalho, A (Author) Other

Conference proceedings International

Title: Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007

Pages: 622-627

20th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007

Key West, FL, 7 May 2007 through 9 May 2007

Indexing

Scopus - 2 Citations

Other information

Authenticus ID: P-007-K2N

Abstract (EN): Recent work highlights advantages in decomposing multiclass decision problems into multiple binary problems. Several strategies have been proposed for this decomposition. The most frequently investigated are All-vs-All, One-vs-All and the Error correction output codes (ECOC). ECOC are binary words (codewords) and can be adapted to be used in classifications problems. They must, however, comply with some specific constraints. The codewords can have several dimensions for each number of classes to be represented. These dimensions grow exponentially with the number of classes of the multiclass problem. Two methods to choose the dimension of a ECOC, which assure a good trade-off between redundancy and error correction capacity, are proposed in this paper. The methods are evaluated in a set of benchmark classification problems. Experimental results show that they are competitive against conventional multiclass decomposition methods. Copyright

Language: English

Type (Professor's evaluation): Scientific