Learning Fuzzy Linguistic Models from Low Quality Data by Genetic Algorithms

Incremental rule base learning techniques can be used to learn models and classifiers from interval or fuzzy-valued data. These algorithms are efficient when the observation error is small. This paper is about datasets with medium to high discrepancies between the observed and the actual values of the variables, such as those containing missing values and coarsely discretized data. We will show that the quality of the iterative learning degrades in this kind of problems, and that it does not make full use of all the available information. As an alternative, we propose a new implementation of a mutiobjective Michigan-like algorithm, where each individual in the population codifies one rule and the individuals in the Pareto front form the knowledge base.