Keywords
Confusion matrixConfusionComputer scienceArtificial intelligenceClassifier (UML)Measure (data warehouse)Binary classificationMachine learningBinary numberNatural language processingSet (abstract data type)Class (philosophy)Data miningPattern recognition (psychology)MathematicsSupport vector machineArithmetic
Affiliated Institutions
Related Publications
Using Maximum Entropy for Text Classification
This paper proposes the use of maximum entropy techniques for text classification. Maximum entropy is a probability distribution estimation technique widely used for a variety o...
An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages
The growing problem of unsolicited bulk e-mail, also known as "spam", has generated a need for reliable anti-spam e-mail filters. Filters of this type have so far been based mos...
Classification assessment methods
Classification techniques have been applied to many applications in various fields of sciences. There are several ways of evaluating classification algorithms. The analysis of s...
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
Abstract Background To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the ...
Support vector machines
My first exposure to Support Vector Machines came this spring when heard Sue Dumais present impressive results on text categorization using this analysis technique. This issue's...
Publication Info
- Year
- 2009
- Type
- article
- Volume
- 45
- Issue
- 4
- Pages
- 427-437
- Citations
- 5642
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
5642
OpenAlex
Cite This
Marina Sokolova,
Guy Lapalme
(2009).
A systematic analysis of performance measures for classification tasks.
Information Processing & Management
, 45
(4)
, 427-437.
https://doi.org/10.1016/j.ipm.2009.03.002
Identifiers
- DOI
- 10.1016/j.ipm.2009.03.002