Christoforos Anagnostopoulos

computational statistics, machine learning and streaming data analysis (download my cv or link in)

  • personal information and research interests
  • publications
  • invited talks
  • code (NEW)
  • teaching - Statistical Modelling II
  • teaching - Official Statistics
  • teaching - Graphical Modelling
  • opinions and news (twitter feed)

The H-measure of classification performance

The H-measure is a coherent alternative to the Area Under the ROC Curve (AUC) for measuring classification performance. Authors: David J. Hand, Christoforos Anagnostopoulos (maintainer).

  • R package (MAC OS / LINUX): hmeasure_1.0.tar.gz (see readme.txt for instructions).
  • R package (WINDOWS): hmeasure_1.0.zip (instructions as above).
  • Matlab v0.1 implementation: hmeasure.m and an example file hmeasure_example.m
Above code is still in beta mode -- please report any bugs! References:
  • Hand, D.J. and Anagnostopoulos, C., A better Beta for the H measure of classification performance, arXiv preprint, http://arxiv.org/abs/1202.2564
  • D.J. Hand. Measuring classifier performance: a coherent alternative to the area under the ROC curve. Machine Learning, 77:103–123, 2009.
  • D.J. Hand. Evaluating diagnostic tests: the area under the ROC curve and the balance of errors. Statistics in Medicine, 29:1502–1510, 2010.

Code for streaming classification

coming soon


canagnos [at] imperial [dot] ac [dot] uk