Discovering Knowledge from Predominantly Repetitive Data by InterCriteria Analysis

  • Olympia RoevaEmail author
  • Nikolay Ikonomov
  • Peter Vassilev
Part of the Studies in Computational Intelligence book series (SCI, volume 795)


In this paper, InterCriteria analysis (ICrA) approach for finding existing or unknown correlations between multiple objects against multiple criteria is considered. Five different algorithms for InterCriteria relations calculation, namely \(\mu \)-biased, Balanced, \(\nu \)-biased, Unbiased and Weighted, are compared using a new cross-platform software for ICrA approach – ICrAData. The comparison have been done based on numerical data from series of model parameter identification procedures. Real experimental data from an E. coli fed-batch fermentation process are used. In order to estimate the model parameters (\(\mu _{max}, k_{S}\) and \(Y_{S/X}\)) fourteen differently tuned Genetic algorithms are applied. ICrA is executed to evaluate the relation between the model parameters, objective function value and computation time. Some useful conclusions with respect to the selection of the appropriate ICrA algorithm for a given data are established. The considered example illustrates the applicability of the ICrA algorithms and demonstrates the correctness of the ICrA approach.


InterCriteria Analysis Weighted algorithm Genetic algorithms E. coli Fermentation process 



Work presented here is partially supported by the National Scientific Fund of Bulgaria under grants DFNI-DN 02/10 “New Instruments for Knowledge Discovery from Data, and their Modelling”.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Olympia Roeva
    • 1
    Email author
  • Nikolay Ikonomov
    • 2
  • Peter Vassilev
    • 1
  1. 1.Institute of Biophysics and Biomedical EngineeringBulgarian Academy of SciencesSofiaBulgaria
  2. 2.Department of Analysis, Geometry and TopologyInstitute of Mathematics and Informatics, Bulgarian Academy of SciencesSofiaBulgaria

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