Abstract
This chapter describes a comprehensive granular model for decision making with complex data. This granular model first uses information decomposition to form a horizontal set of granules for each of the data instances. Each granule is a partial view of the corresponding data instance; and aggregately all the partial views of that data instance provide a complete representation for the instance. Then, the decision making based on the original data can be divided and distributed to decision making on the collection of each partial view. The decisions made on all partial views will then be aggregated to form a final global decision. Moreover, on each partial view, a sequential M+1 way decision making (a simple extension of Yao’s 3-way decision making) can be carried out to reach a local decision. This chapter further categorizes stock price predication problem using the proposed decision model and incorporates the MLVS model for biological sequence classification into the proposed decision model. It is suggested that the proposed model provide a general framework to address the complexity and volume challenges in big data analytics.
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Xie, Y., Johnsten, T., Raghavan, V.V., Benton, R.G., Bush, W. (2015). A Comprehensive Granular Model for Decision Making with Complex. In: Pedrycz, W., Chen, SM. (eds) Granular Computing and Decision-Making. Studies in Big Data, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-16829-6_2
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DOI: https://doi.org/10.1007/978-3-319-16829-6_2
Publisher Name: Springer, Cham
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