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Multi-Objective Biclustering: When Non-dominated Solutions are not Enough

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Journal of Mathematical Modelling and Algorithms

Abstract

The biclustering technique was developed to avoid some of the drawbacks presented by standard clustering techniques, such as their impossibility of finding correlating data under a subset of features, and, consequently, to allow the extraction of more accurate information from datasets. Given that biclustering requires the optimization of at least two conflicting objectives (residue and volume) and that multiple independent solutions are desirable as the outcome, a few multi-objective evolutionary algorithms for biclustering were proposed in the literature. However, these algorithms only focus their search in the generation of a global set of non-dominated biclusters, which may be insufficient for most of the problems as the coverage of the dataset can be compromised. In order to overcome such problem, a multi-objective artificial immune system capable of performing a multipopulation search, named MOM-aiNet, was proposed. In this work, the MOM-aiNet algorithm will be described in detail, and an extensive set of experimental comparisons will be performed, with the obtained results of MOM-aiNet being confronted with those produced by the popular CC algorithm, by another immune-inspired approach for biclustering (BIC-aiNet), and by the multi-objective approach for biclustering proposed by Mitra & Banka.

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Authors and Affiliations

  1. Laboratory of Bioinformatics and Bioinspired Computing (LBiC), Department of Computer Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering (FEEC), University of Campinas (Unicamp), Av. Albert Einstein – 400 – Building G2 – Room LE 14G, 13083–852, P. O. Box 6101, Campinas, São Paulo, Brazil

    Guilherme Palermo Coelho, Fabrício Olivetti de França & Fernando J. Von Zuben

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  1. Guilherme Palermo Coelho
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  2. Fabrício Olivetti de França
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  3. Fernando J. Von Zuben
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Correspondence to Guilherme Palermo Coelho.

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Coelho, G.P., de França, F.O. & Von Zuben, F.J. Multi-Objective Biclustering: When Non-dominated Solutions are not Enough. J Math Model Algor 8, 175–202 (2009). https://doi.org/10.1007/s10852-009-9102-8

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  • DOI: https://doi.org/10.1007/s10852-009-9102-8

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