Optimizing Mixed Fuzzy-Rule Formation by Controlled Evolutionary Strategy

摘要

Machine learning algorithms are heavily applied to address many challenges in various fields. This paper specifically takes a look at use cases from the health sector, as well as the industry 4.0 sector. In both cases, the knowledge about the classification process is as important as the classification itself. One current problem is the disregard of expert knowledge provided by adept human beings. In practice, it is possible and also feasible to learn similar knowledge with machine learning algorithms like artificial neural networks (ANNs) or support vector machines (SVMs). However, time and money could be saved if this expert knowledge was used directly. Right now, this is only possible with more transparent algorithms like rule-based systems or decision trees, where knowledge can be incorporated relatively easily. The approach of this paper shows that rules generated by a mixed fuzzy-rule formation algorithm can be optimized by applying a controlled evolutionary strategy while maintaining the interpretability of the decision-making process. The evaluation is performed by executing the evolutionary strategy proposed in this paper on data from two different industries.