Peter A. Flach
University of Bristol, UK
Tutorial Notes: PDF | Part 1 | Part 2 | Part 3
Receiver Operating Characteristics (ROC) Analysis originated from signal detection theory, as a model of how well a receiver is able to detect a signal in the presence of noise. Its key feature is the distinction between hit rate (or true positive rate) and false alarm rate (or false positive rate) as two separate performance measures. ROC analysis has also widely been used in medical data analysis to study the effect of varying the threshold on the numerical outcome of a diagnostic test. It has been introduced to machine learning relatively recently, in response to classification tasks with varying class distributions or misclassification costs (hereafter referred to as skew). ROC analysis is set to cause a paradigm shift in machine learning. Separating performance on classes is almost always a good idea from an analytical perspective. For instance, it can help us to
The goal of this tutorial is to develop the ROC perspective in a systematic way, demonstrating the many faces of ROC analysis in machine learning.
PhD students and machine learning novices will profit from a gentle introduction to ROC analysis for model evaluation and selection and achieve a better understanding of machine learning metrics. Furthermore, they will be motivated to apply the ROC perspective to their own work. More experienced machine learning researchers, who may already be familiar with the more basic material on model evaluation and selection, will benefit from the fresh perspective on machine learning that the tutorial provides, and perhaps be encouraged to tackle some of the open problems in their own research.
After attending this tutorial, participants will be able to
Only basic knowledge of symbolic classification (decision trees, rule learners) and probabilistic classification (naive Bayes) is expected, at the level of Mitchell or Witten & Frank (one-slide introductions will be provided). It should be noted that most of the techniques covered are not restricted to these classification paradigms.
The material will be presented in two 90-minute blocks with a break in between. The first half is foundational and covers the basics of ROC analysis from the machine learning literature and related fields. The second half deals with more advanced work including some of my own.
The material will mostly be presented using computer-projected PowerPoint slides. In addition, I will make use of the ROC graph software we developed in Bristol .
 F. Provost and T. Fawcett (2001). Robust classification for imprecise environments. Machine Learning, 42, 203-231.
 T. Fawcett (2003). ROC graphs: Notes and practical considerations for data mining researchers . Tech report HPL-2003-4. HP Laboratories, Palo Alto, CA, USA.
 C. Drummond and R.C. Holte (2000). Exploiting the cost (in)sensitivity of decision tree splitting criteria. In P. Langley, editor, Proc. 17th International Conference on Machine Learning (ICML'00), pp. 239-246.
 C.J. Van Rijsbergen (1979). Information retrieval. London: Butterworths.
 A. Martin, G. Doddington, T. Kamm, M. Ordowski, and M. Przybocki (1997). The DET curve in assessment of detection task performance. In Proc. EuroSpeech, pp. 1895-1898.
 P.A. Flach (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.
 J. Fürnkranz and P.A. Flach (2003). An analysis of rule evaluation metrics. In T. Fawcett and N. Mishra, editors, Proc. 20th International Conference on Machine Learning (ICML'03), pp. 202-209. AAAI Press.
 J. Fürnkranz and P.A. Flach (forthcoming). ROC 'n' rule learning -- towards a better understanding of covering algorithms. Machine Learning, accepted for publication.
 R. Vilalta and D. Oblinger (2000). A quantification of distance-bias between evaluation metrics in classification. In P. Langley, editor, Proc. 17th International Conference on Machine Learning (ICML'00), pp. 1087-1094. Morgan Kaufmann.
 P.A. Flach and S. Wu (2003). Reparing concavities in ROC curves. In J.M. Rossiter and T.P. Martin, editors, Proc. 2003 UK workshop on Computational Intelligence (UKCI'03), pp. 38-44. University of Bristol.
 C. Ferri, P.A. Flach, and J. Hernández-Orallo (2002). Learning Decision Trees Using the Area Under the ROC Curve. In C. Sammut and A. Hoffmann, editors, Proc. 19th International Conference on Machine Learning (ICML'02), pp. 139-146. Morgan Kaufmann.
 R.E. Steuer (1986). Multiple Criteria Optimization: Theory, Computation and Application. Wiley, New York.
 D.J. Hand and R.J. Till (2001). A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems, Machine Learning, 45, 171-186.
 N. Lachiche and P.A. Flach (2003). Improving accuracy and cost of two-class and multi-class probabilistic classifiers using ROC curves. In T. Fawcett and N. Mishra, editors, Proc. 20th International Conference on Machine Learning (ICML'03), pp. 416-423. AAAI Press.