Lazy Classification Algorithms Based on Deterministic and Inhibitory Rules

In the paper, two families of lazy classification algorithms of polynomial time complexity are considered. These algorithms are based on deterministic (with a relation “attribute = value” on the right hand side) and inhibitory (with a relation “attribute 6= value” on the right hand side) rules, but the direct generation of rules is not required. Instead of this, the considered algorithms extract efficiently for a new object some information about the set of rules which is next used by a decision-making procedure. Results of experiments show that the performance of algorithms based on inhibitory rules is in many cases better than the performance of algorithms based on deterministic rules.