Abstract
A hybrid classification system is a system composed of several intelligent techniques such that the inherent limitations of one individual technique be compensated for by the strengths of another technique. In this paper, we investigate the outline of a hybrid diagnostic system for Attention Deficit Disorder (ADD) in children. This system uses Rough Sets (RS) and Modified Rough Sets (MRS) to induce rules from examples and then uses our modified genetic algorithms to globalize the rules. Also, the classification capability of this hybrid system was compared with the behavior of (a) another hybrid classification system using RS, MRS, and the “dropping condition” approach, (b) the Interactive Dichotomizer 3 (ID3) approach, and (c) a basic genetic algorithm.
The results revealed that the global rules generated by the hybrid system are more effective in classification of the testing dataset than the rules generated by the above approaches.
| Original language | American English |
|---|---|
| Title of host publication | Rough Sets and Data Mining: Analysis of Imprecise Data |
| State | Published - Jan 1997 |
Disciplines
- Engineering
- Computer Sciences
Keywords
- Approximate space
- Attention deficit disorder
- Decision conflict
- Genetic algorithm
- Local rule