A DNA-based algorithm for minimizing decision rules: A rough sets approach

Ikno Kim*, Yu Yi Chu, Junzo Watada, Jui Yu Wu, Witold Pedrycz

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)


    Rough sets are often exploited for data reduction and classification. While they are conceptually appealing, the techniques used with rough sets can be computationally demanding. To address this obstacle, the objective of this study is to investigate the use of DNA molecules and associated techniques as an optimization vehicle to support algorithms of rough sets. In particular, we develop a DNA-based algorithm to derive decision rules of minimal length. This new approach can be of value when dealing with a large number of objects and their attributes, in which case the complexity of rough-sets-based methods is NP-hard. The proposed algorithm shows how the essential components involved in the minimization of decision rules in data processing can be realized.

    Original languageEnglish
    Article number6048012
    Pages (from-to)139-151
    Number of pages13
    JournalIEEE Transactions on Nanobioscience
    Issue number3
    Publication statusPublished - 2011 Sept


    • Data processing
    • decision rules
    • DNA-based algorithm
    • knowledge support system
    • rough sets

    ASJC Scopus subject areas

    • Pharmaceutical Science
    • Medicine (miscellaneous)
    • Bioengineering
    • Computer Science Applications
    • Biotechnology
    • Biomedical Engineering
    • Electrical and Electronic Engineering


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