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Toplam kayıt 17, listelenen: 1-10
Tree Ensembles on the induced discrete space
(Institute of Electrical and Electronics Engineers Inc., 2016-05)
Decision trees are widely used predictive models in machine learning. Recently, K-tree is proposed, where the original discrete feature space is expanded by generating all orderings of values of k discrete attributes and ...
VC-dimension of univariate decision trees
(IEEE-INST Electrical Electronics Engineers Inc, 2015-02-25)
In this paper, we give and prove the lower bounds of the Vapnik-Chervonenkis (VC)-dimension of the univariate decision tree hypothesis class. The VC-dimension of the univariate decision tree depends on the VC-dimension ...
Parallel univariate decision trees
(Elsevier B.V., 2007-05-01)
Univariate decision tree algorithms are widely used in data mining because (i) they are easy to learn (ii) when trained they can be expressed in rule based manner. In several applications mainly including data mining, the ...
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 ...
Omnivariate rule induction using a novel pairwise statistical test
(IEEE Computer Soc, 2013-09)
Rule learning algorithms, for example, RIPPER, induces univariate rules, that is, a propositional condition in a rule uses only one feature. In this paper, we propose an omnivariate induction of rules where under each ...
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 ...
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 ...
Model selection in omnivariate decision trees using Structural Risk Minimization
(Elsevier Science Inc, 2011-12-01)
As opposed to trees that use a single type of decision node, an omnivariate decision tree contains nodes of different types. We propose to use Structural Risk Minimization (SRM) to choose between node types in omnivariate ...
A novel kernel to predict software defectiveness
(Elsevier Science Inc, 2016-09)
Although the software defect prediction problem has been researched for a long time, the results achieved are not so bright. In this paper, we propose to use novel kernels for defect prediction that are based on the ...