A linguistic CMAC equivalent to a Linguistic Decision Tree for classification

Linguistic Decision Trees based on label semantics have been used as a classifier or predictor in many areas. A linguistic decision tree presents information propagation from input attributes to a goal variable based on transparent linguistic rules. The relationship between input attributes and the goal variable is often highly nonlinear. 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. 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.