Incomplete Information of Decision Support System for Planting Material Selection

Incomplete information introduces uncertainty into decision modeling evaluation, since it is no longer obvious which objects should be retrieved. In agriculture environment where ecological databases are distributed on several information servers, the informative features such as planting material, weather and fertilizer should be exploited in optimal way with supported informative and relevant requirements derived from the expert. Many real-world data sets which have missing values attempt to impute some values, while many statistical and learning methods simply deleting directly the missing values. However, we measure the degree of uncertainty to rank objects for presentation to a user and measure the observed value into fuzzy representation to generate simple and comprehensible decision rule sets as `information´.