A fuzzy evolutionary framework for combining ensembles

We propose an evolutionary framework for the production of fuzzy rule bases where each rule executes an ensemble of predictors. The architecture, the rule base and the composition of the ensembles are evolved over time. To achieve this, we employ a context-free grammar within a hybrid genetic programming system using a multi-population model. As base predictors, multilayer perceptron neural networks and support vector machines are available. We apply the system to several function approximation and regression tasks and compare the results with recent research and state-of-the-art models. We conclude that the proposed architecture is competitive and has a number of very desirable features supporting automation of predictive model building and their adaptation over time. Finally, we suggest further potential research directions.

Figure optionsDownload as PowerPoint slideHighlights
► We propose an integrated framework for generating fuzzy rule-based systems that combine ensembles.
► We use neural networks and support vector machines as learners and genetic programming to evolve combinations.
► We test the proposed methodologies over a series of artificial and real-world datasets.