Konu "Decision trees" için listeleme
Toplam kayıt 22, listelenen: 1-20
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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 ... -
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 ... -
Calculating the VC-dimension of decision trees
(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 ... -
Feature extraction from discrete attributes
(IEEE, 2010)In many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we extract new features by combining k discrete attributes, where for each ... -
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 ... -
Müşterilerin GSP analizi kullanarak kümelenmesi
(Institute of Electrical and Electronics Engineers Inc., 2018-07-05)Bu çalışma ile mevcut misafir ve rezervasyon verisi kullanılarak doğal öbeklenmeleri tespit ederek misafir davranışları tespit ettik. Ayrıca verilen hizmetleri ve satış stratejilerini bu davranışlara göre özelleştirdik. ... -
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 ... -
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 ... -
On the VC-dimension of univariate decision trees
(2012)In this paper, we give and prove lower bounds of the VC-dimension of the univariate decision tree hypothesis class. The VC-dimension of the univariate decision tree depends on the VC-dimension values of its subtrees and ... -
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 ... -
Re-mining item associations: Methodology and a case study in apparel retailing
(Elsevier Science BV, 2011-12)Association mining is the conventional data mining technique for analyzing market basket data and it reveals the positive and negative associations between items. While being an integral part of transaction data, pricing ... -
Regularizing soft decision trees
(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 ... -
Reviewing the effects of spatial features on price prediction for real estate market: Istanbul case
(IEEE, 2022-09-16)In the real estate market, spatial features play a crucial role in determining property appraisals and prices. When spatial features are considered, classification techniques have been rarely studied compared to regression, ... -
Searching for the optimal ordering of classes in rule induction
(IEEE, 2012-11-15)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 the order of increasing ... -
Soft decision trees
(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/?-sensitive loss
(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) ... -
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 ... -
Tweet sentiment analysis for cryptocurrencies
(IEEE, 2021-10-13)Many traders believe in and use Twitter tweets to guide their daily cryptocurrency trading. In this project, we investigated the feasibility of automated sentiment analysis for cryptocurrencies. For the study, we targeted ... -
Univariate decision tree induction using maximum margin classification
(Oxford Univ Press, 2012-03)In many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we propose a new decision tree learning algorithm called univariate margin ... -
Univariate margin tree
(Springer, 2010)In many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we propose a new decision tree learning algorithm called univariate margin ...