Uncertainty Analysis Using Fuzzy Sets for Decision Support System

In agricultural domain application, it is becoming increasingly important to preserve planting material behavior when interact with an environment that is not under its control. However, the uncertainty always inherent such inaccurate decisions when a present of incomplete information in sampling data set (Chao, et al., 2005; Latkowski, 2002; Michelini, et al., 1995). As a result, the proper decision may need to adapt changes in their environment by adjusting its own behavior. Many different methods for dealing uncertainty have been developed. This research work proposes incomplete information with fuzzy representation in objective function for decision modeling. Firstly, we integrate expert knowledge and planting material data to provide meaningful training data sets. Secondly, fuzzy representation is used to partition data by taking full advantages of the observed information to achieve the better performance. Finally, we optimally generalize decision tree algorithms using decision tree technique to provide simpler and more understandable models. The output of this intelligent decision system can be highly beneficial to users in designing effective policies and decision making.