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Erschienen in:
2018 | OriginalPaper | Buchkapitel
R Implementation of Bayesian Decision Theoretic Rough Set Model for Attribute Reduction
verfasst von : Utpal Pal, Sharmistha Bhattacharya (Halder), Kalyani Debnath
Erschienen in: Industry Interactive Innovations in Science, Engineering and Technology
Verlag: Springer Singapore
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Abstract
Bayesian Decision Theoretic Rough Set (BDTRS) model is a significant advancement in the field of attribute reduction of an information system. However, a lack of the related software for implementing this model can be observed preventing their use in practice. In this paper, the BDTRS model for attribute reduction is further studied and implemented using R programming language as functions. These new R functions are further compared with few existing sophisticated rough set based attribute reduction methods (R functions), available within “RoughSets” package of R. For comparative analysis, secondary data sets from UCI Machine Learning repository has been used. Improved results have been achieved by the implemented BDTRS-based R functions compared to other existing functions. The implemented BDTRS model may now perform attribute reduction for high-dimensional large size practical field data sets which was not possible earlier.