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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 ...
İngi?li?zce-Türkçe i?stati?sti?ksel maki?ne çevi?ri?si?nde bi?çi?m bi?li?m kullanımı
(IEEE, 2012-04-18)
Bu çalışmada, İngilizce-Türkçe dil ikilisi için biçimbilimsel çözümleme yardımı ile SIU dermecesi üzerinde istatistiksel makine çevirisi denemeleri yapılmıştır. Kelime biçimlerinin baz alındığı çeviri denemeleri İngilizce-Türkçe ...
Mapping classifiers and datasets
(Pergamon-Elsevier Science Ltd, 2011-04)
Given the posterior probability estimates of 14 classifiers on 38 datasets, we plot two-dimensional maps of classifiers and datasets using principal component analysis (PCA) and Isomap. The similarity between classifiers ...
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 ...
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 ...
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 ...
Software defect prediction using Bayesian networks
(Springer, 2014-02)
There are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics ...
On the feature extraction in discrete space
(Elsevier Sci Ltd, 2014-05)
In many pattern recognition applications, feature space expansion is a key step for improving the performance of the classifier. In this paper, we (i) expand the discrete feature space by generating all orderings of values ...
Budding trees
(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 ...
Quadratic programming for class ordering in rule induction
(Elsevier Science BV, 2015-03-01)
Separate-and-conquer type rule induction algorithms such as Ripper, solve a K>2 class problem by converting it into a sequence of K - 1 two-class problems. As a usual heuristic, the classes are fed into the algorithm in ...