Now showing items 1-5 of 5
Design and analysis of classifier learning experiments in bioinformatics: survey and case studies
(IEEE COMPUTER SOC, 2012-12)
In many bioinformatics applications, it is important to assess and compare the performances of algorithms trained from data, to be able to draw conclusions unaffected by chance and are therefore significant. Both the design ...
Incremental construction of classifier and discriminant ensembles
(ELSEVIER SCIENCE INC, 2009-04-15)
We discuss approaches to incrementally construct an ensemble. The first constructs an ensemble of classifiers choosing a subset from a larger set, and the second constructs an ensemble of discriminants, where a classifier ...
Eigenclassifiers for combining correlated classifiers
(ELSEVIER SCIENCE INC, 2012-03-15)
In practice, classifiers in an ensemble are not independent. This paper is the continuation of our previous work on ensemble subset selection [A. Ulas, M. Semerci, O.T. Yildiz, E. Alpaydin, Incremental construction of ...
Cost-conscious comparison of supervised learning algorithms over multiple data sets
(ELSEVIER SCI LTD, 2012-04)
In the literature, there exist statistical tests to compare supervised learning algorithms on multiple data sets in terms of accuracy but they do not always generate an ordering. We propose Multi(2)Test, a generalization ...
Bagging soft decision trees
(Springer Verlag, 2016)
The decision tree is one of the earliest predictive models in machine learning. In the soft decision tree, based on the hierarchical mixture of experts model, internal binary nodes take soft decisions and choose both ...