While methods for comparing two learning algorithms on a single data set have been scrutinized for quite some time already, the issue of statistical tests for comparisons of more ...
Accurate, well-calibrated estimates of class membership probabilities are needed in many supervised learning applications, in particular when a cost-sensitive decision must be mad...
Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited...
Bayesian classifiers such as Naive Bayes or Tree Augmented Naive Bayes (TAN) have shown excellent performance given their simplicity and heavy underlying independence assumptions....
Classification fusion combines multiple classifications of data into a single classification solution of greater accuracy. Feature extraction aims to reduce the computational cost ...
Behrouz Minaei-Bidgoli, Gerd Kortemeyer, William F...