Strengths of Fuzzy Techniques in Data Science

Abstract

We show that many existing fuzzy methods for machine learning and data mining contribute to providing solutions to data science challenges, even though statistical approaches are often presented as major tools to cope with big data and modern user expectations of their exploitation. The multiple capacities of fuzzy and related knowledge representation methods make them inescapable to deal with various types of uncertainty inherent in all kinds of data.

Conceptualization and Non-Relational Implementation of Ontological and Epistemic Vagueness of Information in Digital Humanities

“…For instance, certainty qualifiers could be seen as fuzzy characterizations of information. For theses reason, we are also considering fuzzy sets and levels of set membership [32][33][34] or similar rule-based logic mechanisms [32] for improving specific details of the implementation of vagueness.…”

“…Regarding strongly mathematically approaches, they start from similar paradigms to the previous ones (based on margins of error) such as the interval predictor models [29], models that estimate regions of uncertainty of the contained information. A less error-focused approach corresponds to the fuzzy logic subdiscipline [30,31], which develop specific techniques (e.g., fuzzy sets and probability degrees, rule bases, linguistic summaries as fuzzy descriptions of variables or fuzzy quantifiers, and similarity measures) [31][32][33][34] for the modelling of vague aspects of the information. All these techniques contemplate the richness that both types of vagueness bring to the information models and their software applications [32].…”