Genetic Optimization of Meta-Learning Schemes for Context-Based Fault Detection

Abstract

The paper is focused on the problem of performance optimization of meta-learning schemes for context-based fault detection. The context-based reasoning is developed to increase the effectiveness of well-known classification methods due to the need for designing high-efficient fault diagnosis systems. The most important problem to solve in this approach is to find optimal structures as well as optimal values of behavioural parameters of component classifiers. This problem has been formulated as a multi-objective optimization task, and in the next step, the global criterion method was adapted for expressing the meta-criterion function taking into account different statistical measures (objectives) obtained from a table of confusion. It was decided to make use of the generational genetic algorithm in order to search for the optimal solution. The subject of the case study was the scraper conveyor simulator. A few variants of the meta-learning scheme for context-based fault detection were elaborated using common machine learning methods such as decision tree, naive Bayes, Bayesian network and k-nearest neighbours. The obtained results prove that the proposed approach has both theoretical and practical relevance and thus it should find potential applications in real-world conditions.