Improving Rule Induction Precision for Automated Annotation by Balancing Skewed Data Sets

Author:

Batista Gustavo E. A. P. A.,Monard Maria C.,Bazzan Ana L. C.

Publisher

Springer Berlin Heidelberg

Reference11 articles.

1. Batista, G.E.A.P.A., Bazan, A.L., Monard, M.C.: Balancing Training Data for Automated Annotation of Keywords: a Case Study. In: Proceedings of the Second Brazilian Workshop on Bioinformatics, pp. 35–43 (2003)

2. Bazzan, A.L.C., Ceroni, S., Engel, P.M., Schroeder, L.F.: Automatic Annotation of Keywords for Proteins Related to Mycoplasmataceae Using Machine Learning Techniques. Bioinformatics 18(S2), S1–S9 (2002)

3. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic Minority Over-sampling Technique. Jounal of Artificial Intelligence Research 16, 321–357 (2002)

4. Hand, D.J.: Construction and Assessment of Classification Rules. John Wiley and Sons, Chichester (1997)

5. Kohavi, R., Sommerfield, D., Dougherty, J.: Data Mining Using MLC++: A Machine Learning Library in C++. International Journal on Artificial Intelligence Tools 6(4), 537–566 (1997)

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