Land evaluation algorithms based on simplified fuzzy classification association rules and grouping fuzzy decision

To improve the intelligibility and efficiency of knowledge expression for the land evaluation, a land evaluation method combining simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. To reduce the complexity of the land evaluation models and improve the efficiency and intelligibility of fuzzy classification association rules further, an algorithm to eliminate redundant rules for obtaining the simplified fuzzy classification association rules is presented. In addition, considering the challenge of a few samples that are difficult to classify the process of fuzzy decision, an iterative algorithm for grouping fuzzy decision for datasets is discussed. The results of experiments demonstrate that by using only 32 simplified fuzzy classification association rules, accuracy of area of land evaluation can reach 92.2835 percent. It provides a higher precision with the accuracy improved by 5.0039%, comparing with the results of the method combining 32 original fuzzy classification association rules with fuzzy decision when minimum support is 0.005.