Lazy Model-based Supervised Classification Algorithm
- See: Lazy Model-based Learning.
- (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.
- (Melli, 1998) ⇒ Gabor Melli. (1998). “A Lazy Model-based Approach to On-Line Classification." Master's Thesis, Simon Fraser University.
- (Melli, 1996) ⇒ Gabor Melli. (1996). “Ad Hoc Attribute-Value Prediction.” In: Proceedings of AAAI 1996. (AAAI 1996).
- (Friedman et al., 1996) ⇒ Jerome H. Friedman, Ron Kohavi, and Yeogirl Yun. (1996). “Lazy Decision Trees.” In: Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI 1996).