Evaluating Ranking Composition Methods for Multi-Objective Optimization of Knowledge Rules

@article{Giusti2008EvaluatingRC,
  title={Evaluating Ranking Composition Methods for Multi-Objective Optimization of Knowledge Rules},
  author={Rafael Giusti and Gustavo E. A. P. A. Batista and Ronaldo C. Prati},
  journal={2008 Eighth International Conference on Hybrid Intelligent Systems},
  year={2008},
  pages={537-542}
}
Most symbolic classifiers aim at building sets of rules with good coverage and precision. While this is suitable for most applications, they tend to neglect other desirable properties, such as the ability to induce novel knowledge or to show new points of view of well-established concepts. An approach to overcome these limitations involves using a multi-objective evolutionary algorithm to build knowledge rules with specific properties specified by the user. In this paper, we report a research… CONTINUE READING