Output details
11 - Computer Science and Informatics
University of Manchester
Article title
Beyond Fano's Inequality: Bounds on the Optimal F-Score, BER, and Cost-Sensitive Risk and Their Implications
Type
D - Journal article
DOI
-
Title of journal
Journal of Machine Learning Research
Article number
-
Volume number
14
Issue number
1
First page of article
1033
ISSN of journal
1533-7928
Year of publication
2013
URL
-
Number of additional authors
3
Additional information
<13> Maximum likelihood is widely applied in statistics and machine learning, commonly as a proxy to maximise the accuracy of a predictor. This paper is significant as it proves for the first time that similar proxies can be derived for more complex measures such as F-score or cost-sensitive risk. It is shown that maximum likelihood, though widely applied in machine learning to maximise F-score, can lead to catastrophic failures; we suggest an alternative proxy measure that overcomes this. This has implications for any work that has attempted to use maximum likelihood techniques with the intention of optimizing F-score.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
0
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
