IFS-IBA similarity measure in machine learning algorithms

Milošević, Pavle; Petrović, Bratislav; Jeremić, Veljko

doi:10.1016/j.eswa.2017.07.048

Cited by 19 publications

(18 citation statements)

References 42 publications

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“…Operations of conjunction and disjunction are realized in the same manner as in IBA-IFS approach [14,16] and they may be perceived as analogous to the conventional IF operation. The operation of negation is derived on the basis of negation operators for classical fuzzy sets.…”

Section: Iba-based Operations Over Lbifsmentioning

confidence: 99%

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Interpolative Boolean Algebra for Generalizations of Intuitionistic Fuzzy Sets

Milošević

Petrović

2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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The idea of this paper is to investigate intuitionistic fuzzy set (IFS) generalizations and make one step further into that direction by introducing interpolative Boolean algebra (IBA) to Liu's generalized IFS. First, we aim to give an overview of the existing attempts to generalize or alter the definition of IFS and to underline the most general approach. Further, Liu's bipolar I-fuzzy sets (LBIFSs) are introduced as the most universal case of Liu's generalized intuitionistic fuzzy sets. Finally, IBA-based operations are introduced to deal with LBIFS. In the proposed LBIFS-IBA approach, all Boolean laws are fulfilled. The special attention is dedicated to laws of the excluded middle and contradiction.

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Section: Iba-based Operations Over Lbifsmentioning

confidence: 99%

“…[2,3,5]) and practical point of view (e.g. [11,12,14,23,24]). Further, IFS are used as a basis for different enhancements, such as interval valued IFS [4].…”

Section: Introductionmentioning

confidence: 99%

Interpolative Boolean Algebra for Generalizations of Intuitionistic Fuzzy Sets

Milošević

Petrović

2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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“…As GP operator is a subclass of t-norms; the four axioms (commutative, associative, monotone, and boundary condition and non-negativity) are also effective. Among various GP operators, min (GP: ⊗ = min) is only suitable for depicting multi properties of objects [29]. Supposing primary variables Ω = {b 1 , b 1 .…”

Section: Iba Theory On Polygonsmentioning

confidence: 99%

“…With the emergence of non-membership and uncertainty, IFS can provide a richer semantic description compared to fuzzy sets. However, since π A investigations are not as valid as membership and non-membership [29], we define the situation that µ A + v A = 1, and ignore the existence of π A .…”

Section: Iba Theory On Polygonsmentioning

confidence: 99%

“…Details of the similarities measured using the IFS-IBA approach are preserved between objects, consistent with the approach of selecting more proper indices to acquire more crucial details, allowing us to measure similarities between spatial objects. Multiple studies have supported the descriptive power of the IFS-IBA approach [29], through which similarity detection between polygons can be further improved.…”

Section: Introductionmentioning

confidence: 99%

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An Intuitionistic Fuzzy Similarity Approach for Clustering Analysis of Polygons

Chen

et al. 2019

IJGI

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Accurate and reasonable clustering of spatial data results facilitates the exploration of patterns and spatial association rules. Although a broad range of research has focused on the clustering of spatial data, only a few studies have conducted a deeper exploration into the similarity approach mechanism for clustering polygons, thereby limiting the development of spatial clustering. In this study, we propose a novel fuzzy similarity approach for spatial clustering, called Extend Intuitionistic Fuzzy Set-Interpolation Boolean Algebra (EIFS-IBA). When discovering polygon clustering patterns by spatial clustering, this method expresses the similarities between polygons and adjacent graph models. Shape-, orientation-, and size-related properties of a single polygon are first extracted, and are used as indices for measuring similarities between polygons. We then transform the extracted properties into a fuzzy format through normalization and fuzzification. Finally, the similarity graph containing the neighborhood relationship between polygons is acquired, allowing for clustering using the proposed adjacency graph model. In this paper, we clustered polygons in Staten Island, United States. The visual result and two evaluation criteria demonstrated that the EIFS-IBA similarity approach is more expressive compared to the conventional similarity (ConS) approach, generating a clustering result more consistent with human cognition.

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