Multiclass cancer-microarray classification algorithm with Pair-Against-All redundancy
Tipo de documentoTexto en actas de congreso
Fecha de publicación2012
Condiciones de accesoAcceso restringido por política de la editorial
Multiclass cancer classification is still a challenging task in the field of machine learning. A novel multiclass approach is proposed in this work as a combination of multiple binary classifiers. It is an example of Error Correcting Output Codes algorithms, applying data transmission coding techniques to improve the classification as a combination of binary classifiers. The proposed method combines the One Against All, OAA, approach with a set of classifiers separating each class-pair from the rest, called Pair Against All, PAA. The OAA+PAA approach has been tested on seven publicly available datasets. It has been compared with the common OAA approach and with state of the art alternatives. The obtained results showed how the OAA+PAA algorithm consistently improves the OAA results, unlike other ECOC algorithms presented in the literature.
CitaciónBosio, M., Bellot, P., Salembier, P., Oliveras, A. Multiclass cancer-microarray classification algorithm with Pair-Against-All redundancy. A: IEEE International Workshop on Genomic Signal Processing and Statistic. "Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistic". Washington: 2012, p. 1-4.
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