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Discovering Knowledge from Predominantly Repetitive Data by InterCriteria Analysis

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Recent Advances in Computational Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 795))

Abstract

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.

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Acknowledgements

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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Authors and Affiliations

  1. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 105, 1113, Sofia, Bulgaria

    Olympia Roeva & Peter Vassilev

  2. Department of Analysis, Geometry and Topology, Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, 8 Acad. G. Bonchev Str., 1113, Sofia, Bulgaria

    Nikolay Ikonomov

Authors
  1. Olympia Roeva
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  2. Nikolay Ikonomov
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  3. Peter Vassilev
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Corresponding author

Correspondence to Olympia Roeva .

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Editors and Affiliations

  1. Department of Parallel Algorithms, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Stefka Fidanova

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Roeva, O., Ikonomov, N., Vassilev, P. (2019). Discovering Knowledge from Predominantly Repetitive Data by InterCriteria Analysis. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 795. Springer, Cham. https://doi.org/10.1007/978-3-319-99648-6_12

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