SENFIS - Selected Ensemble of Fuzzy Inference Systems
Errison dos Santos Alves, Ricardo Tanscheit, Marley M. B. R. Vellasco · 2019
Fuzzy Inference Systems (SIF) for classification are machine learning models that employ linguistic rules to describe real problems in an interpretable way. However, when dealing with high dimensional input spaces, these systems tend to suffer from scalability problems and computational cost. This work presents the Selected Ensemble of Fuzzy Inference Systems (SENFIS), an automatic SIF model based on previously developed algorithms (AutoFIS-Class and RandomFIS) and composed of ensemble and subsampling strategies to deal with big datasets. Its performance is compared to those of its predecessors and of others similar fuzzy systems, making use of normal and large benchmarks databases for classification. In terms of accuracy, SENFIS performs as well as its predecessors, but at a lower computational cost. In addition, the resulting rule bases are smaller, especially for high dimensional problems.