ROC curve and Neyman Pearsom Criterion
Partly based on the ECE662 Spring 2014 lecture material of Prof. Mireille Boutin.
1. Outline of the slecture
Receiver Operating Characteristic (ROC) curve is often used as an important tool to visualize the performance of a binary classifier. The use of ROC curves can be originated from signal detection theory that developed during World War II for radar analysis [2]. What will be covered in the slecture is listed as:
- A quick example about ROC in binary classification
- Some statistics behind ROC curves
- Neyman-Pearson Criterion
Reference
[1] Mireille Boutin, "ECE662: Statistical Pattern Recognition and Decision Making Processes," Purdue University, Spring 2014.
[2] Jiawei Han. 2005. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
[3] Richard O. Duda, Peter E. Hart, and David G. Stork. 2000. Pattern Classification. Wiley-Interscience.
[4] Detection Theory. http://www.ece.iastate.edu/~namrata/EE527_Spring08/l5c_2.pdf.
[5] The Neyman-Pearson Criterion. http://cnx.org/content/m11548/1.2/.
Questions and comments
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