Detecting and Recognizing Outliers in Datasets via Linguistic Information and Type-2 Fuzzy Logic (original) (raw)

International Journal of Fuzzy Systems

Uncertainty appearing in datasets (stochastic, linguistic, of measurements, etc.), if not handled properly, may negatively affect information analysis or retrieval procedures. One of possible methods of dealing with uncertain (rare, strange, unexampled) data is to treat them as “outliers” or “exceptions”. Among different definitions and algorithms for detecting outliers, we are especially interested in those based on linguistic information represented with type-2 fuzzy logic. We introduce new definitions of outliers in datasets in terms fuzzy properties and linguistically expressed quantities of objects possessing them. Next, new algorithms for detecting outlying objects are presented, to answer whether outliers appear in a dataset or not. Finally, recognition algorithms are presented and exemplified to enumerate particular objects being outliers (e.g., to eliminate them for further considerations). The novelty of this contribution is that we define, detect and recognize outliers us...

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