CAN: chain of nodes approach to direct rule induction

CAN is a heuristic algorithm that employs an information theoretic measure to learn rules. CAN approach distinguishes itself from other approaches by being direct, meaning that there are no intermediate representations, an induced rule is never altered in later stages and only tests that appear in the final solution are generated. In the selection of rule conditions (tests) existing rule induction algorithms do not provide a satisfactory answer to the partitioning of the feature space of discrete feature variables with nonordered qualitative values (i.e., categorical attributes) for multiclass problems. Existing algorithms have exponential complexity in N, where N is the number of feature values. Therefore, heuristic algorithms are employed at this step. An important contribution of this paper is to show that in test selection within CAN framework optimal partitions are achieved in linear time in N for the multiclass case.