Structure of feature spaces related to fuzzy similarity relations as kernels

2014 ◽  
Vol 237 ◽  
pp. 90-95 ◽  
Author(s):  
Degang Chen ◽  
Deli Zhang
Keyword(s):  
Similarity Relations ◽  
Fuzzy Similarity ◽  
Feature Spaces

Comparison of Dispersion Models by Using Fuzzy Similarity Relations

2011 ◽  
pp. 57-67 ◽  
Author(s):  
Angelo Ciaramella ◽  
Angelo Riccio ◽  
Stefano Galmarini ◽  
Giulio Giunta ◽  
Slawomir Potempski
Keyword(s):  
Dispersion Models ◽  
Similarity Relations ◽  
Fuzzy Similarity

Fuzzy Similarity Relations in Decision Making

2020 ◽  
pp. 369-385 ◽  
Author(s):  
Mohamed El Alaoui ◽  
Khalid El Yassini
Keyword(s):  
Decision Making ◽  
Human Knowledge ◽  
Similarity Relations ◽  
Fuzzy Similarity ◽  
Context Of Use

Similarity is an ambiguous term that can be interpreted differently depending on the context of use. In this chapter, the authors review some of its uses before focusing on decision making. Ignoring the uncertainty of human knowledge would be denying a major attribute. Thus, they linked it to the fuzzy context. However, even taking this aspect into consideration, the opinion itself must be relevant. Therefore, the more a decision is similar to other opinions, the more it is coherent. Hence, there is a need to measure the similarity between each couple of expressed opinions.

Granular Region-oriented Fuzzy-Rough based kNN Improvization for Activity Recognition Modeling

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Vijay Borges ◽  
Wilson Jeberson
Keyword(s):  
Pattern Recognition ◽  
Activity Recognition ◽  
Human Activity ◽  
Test Object ◽  
Feature Space ◽  
Nearest Neighbors ◽  
Human Activity Recognition ◽  
Similarity Relations ◽  
Fuzzy Similarity ◽  
Knn Classifier

Activity recognition is a complex task of the Human Computer Interaction (HCI) domain with ever-increasing research interest. Human activity recognition has been specially addressed by the advances in pattern recognition. k-Nearest Neighbors(kNN) is a non-parametric classifier from pattern recognition theory, that mimics human decision making by taking previous experiences into consideration for segregating unknown objects. A novel fuzzy-rough model, based on granular computing for improvisation of the kNN classifier is proposed herewith. In this model, feature-wise fuzzy memberships are generated to fuzzify the feature space of the nearest neighbors of the test object. These neighbors fuzzified feature space are then aggregated into granules, based on their class-belongingness. From these, lower and upper approximation granules are generated using rough set theory to classify the test object. It is shown experimentally that this model outperforms the traditional kNN by 16.43% and Fuzzy-kNN by 10.25%, in the human activity recognition domain. Another novelty is in the efficient use of the fuzzy similarity relations in class-dependent granulated feature space, and, fuzzy-rough lower/upper approximations in the hybridization of the kNN classifier.

Granular computing based on fuzzy similarity relations

2010 ◽  
Vol 15 (6) ◽  
pp. 1161-1172 ◽  
Author(s):  
Chen Degang ◽  
Yang Yongping ◽  
Wang Hui
Keyword(s):  
Granular Computing ◽  
Similarity Relations ◽  
Fuzzy Similarity
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