A Linguistic CMAC vs. a Linguistic Decision Tree for Decision Making

摘要

Cerebellar Model Articulation Controller (CMAC) belongs to the family of feed-forward networks with a single linear trainable layer. A CMAC has the feature of fast learning, and is suitable for modeling any non-linear relationship. Combining label semantics and an original CMAC, a linguistic CMAC based on Mass Assignment on labels is proposed to map the relationship between the attributes and the goal variable that is often highly nonlinear. Linguistic Decision Trees based on label semantics have been used as a decision maker in many areas. A linguistic decision tree presents information propagation from input attributes to a goal variable based on transparent linguistic rules. The proposed LCMAC model is functionally equivalent to a linguistic decision tree, and takes the advantage of fast local training of the original CMAC and the advantage of transparency of a linguistic decision tree.