Knowledge Supported Refinements for Rough Granular Computing: A Case of Life Insurance Industry
Dominance-based rough set approach (DRSA) has been adopted in solving various multiple criteria classification problems with positive outcomes; its advantage in exploring imprecise and vague patterns is especially useful concerning the complexity of certain financial problems in business environment. Although DRSA may directly process the raw figures of data for classifications, the obtained decision rules (i.e., knowledge) would not be close to how domain experts comprehend those knowledge—composed of granules of concepts—without appropriate or suitable discretization of the attributes in practice. As a result, this study proposes a hybrid approach, composes of DRSA and a multiple attributes decision method, to search for suitable approximation spaces of attributes for gaining applicable knowledge for decision makers (DMs). To illustrate the proposed idea, a case of life insurance industry in Taiwan is analyzed with certain initial experiments. The result not only improves the classification accuracy of the DRSA model, but also contributes to the understanding of financial patterns in the life insurance industry.
KeywordsDominance-based rough set approach (DRSA) Multiple attribute decision making (MADM) DEMATEL-based ANP (DANP) Approximation space (AS) Granular computing Financial performance (FP) Life insurance industry
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