Evolutionary Tuning of Combined Multiple Models

  • Gregor Stiglic
  • Peter Kokol
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)


In data mining, hybrid intelligent systems present a synergistic combination of multiple approaches to develop the next generation of intelligent systems. Our paper presents an integration of a Combined Multiple Models (CMM) technique with an evolutionary approach that is used for tuning of parameters. Proposed hybrid classifier was tested in microarray analysis domain. This domain was chosen intentionally, because of the nature of Combined Multiple Models classifiers that are specialized in solving problems with high dimensionality and contain low number of samples. Evolutionary tuning of parameters in combination with validation dataset enables fine tuning of parameters that are usually set to pre-defined values. Using this technique we made another step in leveling the accuracy of comprehensible classifiers to those represented by ensembles of classifiers.


Acute Myeloid Leukemia Acute Lymphoblastic Leukemia Malignant Pleural Mesothelioma Validation Dataset Acute Myeloid Leukemia Sample 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gregor Stiglic
    • 1
  • Peter Kokol
    • 1
  1. 1.Faculty of Electrical Engineering and Computer ScienceUniversity of MariborMariborSlovenia

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