Some pointers to the literature on classifier evaluation

• Fawcett, T. (2003):  ROC graphs: Notes and practical considerations for data mining researchers . Tech report HPL-2003-4. HP Laboratories, Palo Alto, CA, USA.
• Flach, P. A. (2003): The geometry of ROC space: understanding machine learning metrics through ROC isometrics. In T. Fawcett and N. Mishra, editors, Proc. 20th International Conference on Machine Learning (ICML'03), pp. 194-201. AAAI Press.
  
• Flach, P. A. (2004): Slides for ICML2004 Tutorial on "The Many Faces of ROC Analysis in Machine Learning".
  
• Ruschhaupt, M., Huber, W., Poustka, A., Mansmann, U. (2004): A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks. Statistical Applications in Genetics and Molecular Biology 3:1 (2004).
  
• Salzberg, S. L. (1997): On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach. Data Mining and Knowledge Discovery 1:3 (1997), pp. 317-327.