Browsing by Author "Kramer, Stefan"

Now showing items 1-5 of 13

  • Alternating model trees

    Frank, Eibe; Mayo, Michael; Kramer, Stefan (ACM Press, 2015)
    Model tree induction is a popular method for tackling regression problems requiring interpretable models. Model trees are decision trees with multiple linear regression models at the leaf nodes. In this paper, we propose ...
  • Data Quality in Predictive Toxicology: Identification of Chemical Structures and Calculation of Chemical Descriptors

    Helma, Christoph; Kramer, Stefan; Pfahringer, Bernhard; Gottmann, Eva (Environmental health perspectives, 2000)
    Every technique for toxicity prediction and for the detection of structure–activity relationships relies on the accurate estimation and representation of chemical and toxicologic properties. In this paper we discuss the ...
  • Ensembles of nested dichotomies for multi-class problems

    Frank, Eibe; Kramer, Stefan (University of Waikato, Department of Computer Science, 2004-02)
    Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees that recursively split a multi-class ...
  • Experiments in Predicting Biodegradability

    Blockeel, Hendrik; Džeroski, Sašo; Kompare, Boris; Kramer, Stefan; Pfahringer, Bernhard; Van Laer, Wim (Taylor & Francis, 2004)
    This paper is concerned with the use of AI techniques in ecology. More specifically, we present a novel application of inductive logic programming (ILP) in the area of quantitative structure-activity relationships (QSARs). ...
  • Fast conditional density estimation for quantitative structure-activity relationships

    Buchwald, Fabian; Girschick, Tobias; Kramer, Stefan; Frank, Eibe (Association for the Advancement of Artificial Intelligence, 2010)
    Many methods for quantitative structure-activity relationships (QSARs) deliver point estimates only, without quantifying the uncertainty inherent in the prediction. One way to quantify the uncertainy of a QSAR prediction ...