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

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 work that combined evolutionary algorithms and ranking composition methods for multi-objective optimization. In this approach, candidate solutions are built, evaluated and ranked according to their performance in each individual objective. Then rankings are composed into a single ranking which reflects the candidate solutions' ability to solve the multi-objective problem considering all objectives simultaneously. We investigate the behavior of 5 ranking composition methods. These methods are...

• Type: Conference
• Year: 2008
• Where: HIS
• Authors: Rafael Giusti, Gustavo E. A. P. A. Batista, Ronaldo Cristiano Prati
• Keywords: Evolutionary Algorithms, HIS 2008, Information Technology, Most Symbolic Classifiers, Ranking Composition Methods