Learning from Examples Module (LEM) Rule Induction Algorithm

From GM-RKB
(Redirected from LEM-based Algorithm)
Jump to: navigation, search

A Learning from Examples Module (LEM) Rule Induction Algorithm is rule Rule Induction Algorithm that can learn a rule set from training examples.



References

2009a

2009b

No Initial discretization Induction Algorithm Number of obtained rules Percentage of correctly classified examples [%] Percentage of incorrectly classified examples [%] Percentage of non-classified examples [%]
1. None LEM2 178 24 32 44
2. MODLEM 35 87 2 11
3. EXPLORE 5 21 76 3
4. Local Method LEM2 56 91 9 0
5. MODLEM 46 91 9 0
6. EXPLORE 300 74 26 0
Table. 1. Classification results obtained with different algorithms.

Obtained results indicate high efficiency of MODLEM algorithm in case of non-discretized data. The obtained classification accurateness, estimated with 10-fold cross validation technique, was 87 %. Classification accuracy obtained with LEM2 algorithm was, in this case, 24 %, while in case of EXPLORE algorithm, in 21 %. In the case of initial digitalization conducted with help of LEM2 and MODLEM algorithms, identical results were obtained. The lowest accuracy was obtained with EXPLORE algorithm.

2003

2002

2001a

2001b

1997

1992

1991

  • (Chan & Grzymala-Busse, 1991) ⇒ C.C. Chan, and Jerzy W. Grzymala-Busse (1991). "On The Attribute Redundancy And The Learning Programs ID3, PRISM, and LEM2". Department of Computer Science, University of Kansas, TR-91-14, December 1991, 20 pp.

  1. (Pawlak, 1982) ⇒ Z. Pawlak (1992). "Rough Sets". International Journal of Computer and Information Sciences 1982; 11: 341–356.
  2. (Pawlak, 1991) ⇒ Z. Pawlak. "Rough Sets. Theoretical Aspects of Reasoning about Data". Kluwer Academic Publishers.
  3. (Pawlak et al., 1995) ⇒ Z. Pawlak, J.W. Grzymala-Busse, R. Slowinski and W. Ziarko (1995). "Rough Sets". Communications of the ACM 1995; 38: 88– 95