Ensemble Modeling for Bio-medical Applications

  • Christian Merkwirth
  • Jörg Wichard
  • Maciej J. Ogorzałek
Part of the Studies in Computational Intelligence book series (SCI, volume 180)


In this paper we propose to use ensembles of models constructed using methods of Statistical Learning. The input data for model construction consists of real measurements taken in physical system under consideration. Further we propose a program toolbox which allows the construction of single models as well as heterogenous ensembles of linear and nonlinear models types. Several well performing model types, among which are ridge regression, k-nearest neighbor models and neural networks have been implemented. Ensembles of heterogenous models typically yield a better generalization performance than homogenous ensembles. Additionally given are methods for model validation and assessment as well as adaptor classes performing transparent feature selection or random subspace training on large number of input variables. The toolbox is implemented in Matlab and C++ and available under the GPL. Several applications of the described methods and the numerical toolbox itself are described. These include ECG modeling, classification of activity in drug design and ...


Ensemble Modeling Multivariate Adaptive Regression Spline Generalization Error Ensemble Class Stochastic Gradient Descent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Christian Merkwirth
    • 1
  • Jörg Wichard
    • 2
    • 4
  • Maciej J. Ogorzałek
    • 1
    • 3
  1. 1.Department of Information TechnologiesJagiellonian UniversityCracowPoland
  2. 2.Institute of Molecular PharmacologyBerlinGermany
  3. 3.AGH University of Science and TechnologyCracowPoland
  4. 4.Institut für Medizinische InformatikBerlinGermany

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