@article{id:Siminski2021Outlier,
	abstract = "Real life data often suffer from noninformative objects – outliers. 
	These are objects that are not typical in a dataset and can significantly decline 
	the efficacy of fuzzy models. In the paper we analyse neuro-fuzzy systems robust 
	to outliers in classification and regression tasks. We use Fuzzy C-Ordered Means 
	(FCOM) clustering algorithm for scatter domain partition to identify premises 
	of fuzzy rules. The clustering algorithm elaborates typicality of each object. 
	Data items with low typicalities are removed from further analysis. The paper 
	is accompanied by experiments that show the efficacy of our modified neuro-fuzzy 
	system to identify fuzzy models robust to high ratios of outliers.",
	author = "Krzysztof Siminski",
	doi = "10.34768/amcs-2021-0021",
	journal = "International Journal of Applied Mathematics and Computer Science",
	number = "2",
	pages = "303–319",
	title = "An outlier-robust neuro-fuzzy system for classification and regression",
	volume = "31",
	year = "2021"
}