Now showing items 1-5 of 5

  • Omnivariate Rule Induction Using a Novel Pairwise Statistical Test 

    Yıldız, Olcay Taner (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 

    Yıldız, Olcay Taner (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 ...
  • Parallel univariate decision trees 

    Yıldız, Olcay Taner; Dikmen, Onur (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 ...
  • Tree Ensembles on the Induced Discrete Space 

    Yıldız, Olcay Taner (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 

    Yıldız, Olcay Taner (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2015-02)
    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 ...