Abstract
This paper aims to demonstrate that the Spearman rank correlation
coefficient and Kendall's tau are not are not ideally suited for
measuring rank correlation for numerical data that are perturbed by
noise. We propose a robust rank correlation measure on the basis of
fuzzy orderings. The superiority of the new measure is demonstrated
by means of illustrative examples.
| Original language | English |
|---|---|
| Title of host publication | Proc. 5th Conf. of the European Society for Fuzzy Logic and Technology |
| Pages | 321327 |
| Number of pages | 7 |
| Publication status | Published - Sept 2007 |
Fields of science
- 101004 Biomathematics
- 101027 Dynamical systems
- 101028 Mathematical modelling
- 101029 Mathematical statistics
- 101014 Numerical mathematics
- 101015 Operations research
- 101016 Optimisation
- 101017 Game theory
- 101018 Statistics
- 101019 Stochastics
- 101024 Probability theory
- 101026 Time series analysis
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102003 Image processing
- 102004 Bioinformatics
- 102013 Human-computer interaction
- 102018 Artificial neural networks
- 102019 Machine learning
- 103029 Statistical physics
- 106005 Bioinformatics
- 106007 Biostatistics
- 202017 Embedded systems
- 202035 Robotics
- 202036 Sensor systems
- 202037 Signal processing
- 305901 Computer-aided diagnosis and therapy
- 305905 Medical informatics
- 305907 Medical statistics
- 102032 Computational intelligence
- 102033 Data mining
- 101031 Approximation theory