Yazar "Alpaydın, Ahmet İbrahim Ethem" için listeleme
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Bagging soft decision trees
Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim Ethem; İrsoy, Ozan (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 ... -
Budding trees
Yıldız, Olcay Taner; İrsoy, Ozan; Alpaydın, Ahmet İbrahim Ethem (IEEE Computer Soc, 2014-08-24)We propose a new decision tree model, named the budding tree, where a node can be both a leaf and an internal decision node. Each bud node starts as a leaf node, can then grow children, but then later on, if necessary, its ... -
Calculating the VC-dimension of decision trees
Yıldız, Olcay Taner; Aslan, Özlem; Alpaydın, Ahmet İbrahim Ethem (IEEE, 2009)We propose an exhaustive search algorithm that calculates the VC-dimension of univariate decision trees with binary features. The VC-dimension of the univariate decision tree with binary features depends on (i) the ... -
Cost-conscious comparison of supervised learning algorithms over multiple data sets
Ulaş, Aydın; Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim Ethem (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 ... -
Design and analysis of classifier learning experiments in bioinformatics: survey and case studies
İrsoy, Ozan; Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim Ethem (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 ... -
Eigenclassifiers for combining correlated classifiers
Ulaş, Aydın; Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim Ethem (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 ... -
Incremental construction of classifier and discriminant ensembles
Ulaş, Aydın; Semerci, Murat; Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim Ethem (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 ... -
Multivariate statistical tests for comparing classification algorithms
Yıldız, Olcay Taner; Aslan, Özlem; Alpaydın, Ahmet İbrahim Ethem (Springer, Berlin, Heidelberg, 2011)The misclassification error which is usually used in tests to compare classification algorithms, does not make a distinction between the sources of error, namely, false positives and false negatives. Instead of summing ... -
Regularizing soft decision trees
Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim Ethem (Springer, 2013)Recently, we have proposed a new decision tree family called soft decision trees where a node chooses both its left and right children with different probabilities as given by a gating function, different from a hard ... -
Soft decision trees
İrsoy, Ozan; Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim Ethem (IEEE, 2012)We discuss a novel decision tree architecture with soft decisions at the internal nodes where we choose both children with probabilities given by a sigmoid gating function. Our algorithm is incremental where new nodes are ... -
Statistical tests using hinge/ε-sensitive loss
Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim Ethem (Springer-Verlag, 2013)Statistical tests used in the literature to compare algorithms use the misclassification error which is based on the 0/1 loss and square loss for regression. Kernel-based, support vector machine classifiers (regressors) ...