Intuitionistic Fuzzy Set Applications in Pattern Recognition and Medical Diagnosis
Summary
Intuitionistic fuzzy sets (IFSs) extend classical fuzzy sets by assigning to each element both a degree of membership and a degree of non-membership, with the residual uncertainty captured by a hesitation margin. This dual characterization enables nuanced modelling of imprecise or conflicting information, making IFSs especially suited to tasks that involve ambiguous data patterns or uncertain clinical observations. In pattern recognition, IFS-based similarity and distance measures enhance the discrimination of classes in image analysis, gesture recognition and voice classification, where overlapping features and noisy inputs often hinder conventional approaches. In medical diagnosis, IFS frameworks support multi-criteria decision engines that integrate symptom assessments, laboratory values and expert judgments, improving the robustness of diagnostic support systems for conditions ranging from oncology screening to neurological disorder classification. Recent methodological advances have refined the axiomatic foundations of similarity/distance metrics, incorporated interval-valued uncertainty and exploited integrative aggregation operators, collectively strengthening the interpretability and reliability of IFS-driven algorithms in both research and clinical environments.
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Intuitionistic Fuzzy Set Applications in Pattern Recognition and Medical Diagnosis publication trend
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Technical terms
Intuitionistic Fuzzy Set: A set in which each element has both a membership degree and a non-membership degree, with remaining uncertainty termed the hesitation margin.
Membership Degree: A value between 0 and 1 indicating the extent to which an element belongs to a fuzzy set.
Non-membership Degree: A value between 0 and 1 representing the extent to which an element does not belong to a fuzzy set.
Hesitation Margin: The residual uncertainty in an IFS, calculated as 1 minus the sum of membership and non-membership degrees.
Similarity Measure: A function quantifying the closeness between two fuzzy sets, often used to compare patterns or clinical profiles.
Distance Measure: A complementary function that assesses the divergence between two fuzzy sets, useful for classification and anomaly detection.
Interval-Valued Intuitionistic Fuzzy Set: An extension of IFS where membership and non-membership degrees are represented by intervals, capturing additional uncertainty.
Choquet Integral: An aggregation operator that accounts for interactions among criteria when combining fuzzy information into a single measure.
References
- Similarity-Distance Decision-Making Technique and its Applications via Intuitionistic Fuzzy Pairs. Journal of Computational and Cognitive Engineering (2022).
- A Cosine Similarity Measure Based on the Choquet Integral for Intuitionistic Fuzzy Sets and Its Applications to Pattern Recognition. Informatica (2021).
- A Novel Similarity Measure for Interval-Valued Intuitionistic Fuzzy Sets and Its Applications. Symmetry (2018).
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