1. Abellán, J., Masegosa, A.R.: Bagging decision trees on data sets with classification noise. In: Link S., Prade H. (eds.) FoIKS, Lecture Notes in Computer Science, vol. 5956, pp. 248–265. Springer, Heidelberg (2009)
2. Abellán, J., Masegosa, A.R.: Bagging schemes on the presence of class noise in classification. Expert Syst. Appl. 39(8), 6827–6837 (2012)
3. Aha, D.W., Kibler, D.: Noise-tolerant instance-based learning algorithms. In: Proceedings of the 11th International Joint Conference on Artificial Intelligence, Vol. 1, IJCAI’89, pp. 794–799. Morgan Kaufmann Publishers Inc. (1989)
4. Alcalá-Fdez, J., Sánchez, L., García, S., del Jesus, M., Ventura, S., Garrell, J., Otero, J., Romero, C., Bacardit, J., Rivas, V., Fernández, J., Herrera, F.: KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput. Fus. Found. Methodol. Appl. 13, 307–318 (2009)
5. Allwein, E.L., Schapire, R.E., Singer, Y.: Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 1, 113–141 (2000)