Atanassov’s Intuitionistic Fuzzy Sets in Classification of Imbalanced and Overlapping Classes

Szmidt, Eulalia; Kukier, Marta

doi:10.1007/978-3-540-77623-9_26

Cited by 33 publications

(13 citation statements)

References 13 publications

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“…During this step it is necessary to solve the problem of granulation, namely, to pick up a symmetric or an asymmetric model, and the number of intervals for each attribute (cf. Szmidt and Kukier [37], [38], [39]). …”

Section: An Intuitionistic Fuzzy Classifiermentioning

confidence: 92%

“…Bustince et al [12], [11]) and classification of imbalanced and overlapping classes (cf. Szmidt and Kukier [37], [38], [39]), group decision making, negotiations, voting and other situations (cf. Szmidt and Kacprzyk papers).…”

Section: A Brief Introduction To A-ifssmentioning

confidence: 99%

“…Szmidt and Kukier [37], [38], [39]) on the classification of imbalanced and overlapping classes via employing intuitionistic fuzzy sets (Atanassov [1], [3], [4]). We consider a twoclass classification problem ( -relatively small class, and -a bigger class).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An extended numerical analysis of an intuitionistic fuzzy classifier for imbalanced classes

Szmidt

Kacprzyk

Kukier

2013

2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS)

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Recognizing relatively smaller classes (called imbalanced classes) from data is an important task both from a theoretical and practical points of view. In many real world problems smaller classes are usually more interesting from the user point of view but they are more difficult to obtain by a clas- sifier. This paper, which is a continuation of our previous works, discusses a classifier that is based on Atanassovs intuitionistic fuzzy sets (A-IFSs, for short) and shows that it can be a good tool for recognizing imbalanced classes. Our considerations are illustrated on benchmark examples. Special attention is paid to a detailed behavior of the classifier proposed (different measures besides the general accuracy are examined). A simple cross validation method is applied (with 10 experiments).Results are compared with a fuzzy classifier reported to be good in the literature. Also the influence of the type granulation, symmetrical or asymmetrical, and of the number of intervals is considered.

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Section: An Intuitionistic Fuzzy Classifiermentioning

confidence: 92%

Section: A Brief Introduction To A-ifssmentioning

confidence: 99%

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An extended numerical analysis of an intuitionistic fuzzy classifier for imbalanced classes

Szmidt

Kacprzyk

Kukier

2013

2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS)

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“…Bustince et al [15], [14]) and the classification of imbalanced and overlapping classes (cf. Szmidt and Kukier [36], [37], [38]), group decision making (e.g., [4]), genetic algorithms [24], negotiations, voting and other situations (cf. Szmidt and Kacprzyk papers).…”

Section: A Brief Introduction To A-ifssmentioning

confidence: 99%

Intuitionistic Fuzzy Decision Tree: A New Classifier

Bujnowski

Szmidt

Kacprzyk

2015

Advances in Intelligent Systems and Computing

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“…Bustince et al [7], [6]) and classification of imbalanced and overlapping classes (cf. Szmidt and Kukier [27], [28], [29]), group decision making, negotiations, voting and other situations (cf. Szmidt and Kacprzyk papers).…”

Section: A Brief Introduction To the A-ifssmentioning

confidence: 99%

Geometric similarity measures for the intuitionistic fuzzy sets

Szmidt¹,

Kacprzyk²

2013

Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology

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This paper is a continuation of our previous works on geometric similarity measures between Atanassov's intuitionistic fuzzy sets (A-IFSs for short). We consider some traps of the straightforward approach in the case of A-ISs while similarity is understood as a dual concept of a distance. The difficulties are a result of, first, the symmetry of the three terms (the membership, nonmembership and hesitation margin) in an A-IFS element description, and second, of an important role played by those three terms in the definition of the complement of the A-IFS which should be taken into account in the similarity measures.

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Atanassov’s Intuitionistic Fuzzy Sets in Classification of Imbalanced and Overlapping Classes

Cited by 33 publications

References 13 publications

An extended numerical analysis of an intuitionistic fuzzy classifier for imbalanced classes

An extended numerical analysis of an intuitionistic fuzzy classifier for imbalanced classes

Intuitionistic Fuzzy Decision Tree: A New Classifier

Geometric similarity measures for the intuitionistic fuzzy sets

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