Greedy Lazy Model-based Classification Algorithm

A Greedy Lazy Model-based Classification Algorithm is a Lazy Model-based Classification Algorithm that is a Greedy Model-based Classification Algorithm.

• See: Model-based Classification Algorithm, Eager Model-based Classification Algorithm.

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References

2006

• (Malyshkin et al., 2006) ⇒ Vladislav Malyshkin, Ray Bakhramov, Andrey Gorodetsky. (2006). “A Massive Local Rules Search Approach to the Classification Problem.” In: ArXiV.
  • QUOTE: … An interesting attempt to combine model based and lazy instance based learning was presented in (Melli, 1998). In (Melli, 1998) a greedy lazy model–based approach for classification was developed in which the result was a rule tailored to the specific observation. While such an approach gives a simple rule as an answer (which is often much easier to understand than a complex rules set) and often works faster for classification of a single event, it–as every greedy algorithm–is not guaranteed to find the best rule, because the algorithm may not reach the global maximum of the quality criterion and a sub–optimal rule may be returned.

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