Multi-objective evolutionary algorithms based Interpretable Fuzzy models for microarray gene expression data analysis
Zhenyu Wang, Vasile Palade · 2010
We believe the great interpretability of fuzzy models allow fuzzy-based methods to play a very important role in Microarray gene expression data analysis, but the advantages offered by fuzzy-based techniques in this application have not yet been fully explored in the literature. In this paper, we construct Multi-Objective Evolutionary Algorithms based Interpretable Fuzzy (MOEAIF) models for microarray gene expression data analysis. Our novel fuzzy models can significantly decrease the model complexity, and automatically balance the accuracy and interpretability of the models. The experimental studies have shown that relatively simple and small fuzzy rule bases, with satisfactory classification performance, have been successful found for challenging microarray gene expression datasets.