1. Sahar Cherif, Nesrine Baklouti , Adel Alimi and Vaclav Snasel
Paper 140:
Linguistic Representation by Fuzzy Formal Concept and Interval Type-2 Feature Selection By
Sahar Cherif, Nesrine Baklouti , Adel Alimi and Vaclav Snasel

2. Outlines Context Fuzzy Formal Concept Proposed Approach
Experimental Results
Conclusion and perspectives 2

3. Uncertitude, Misunderstandings and Misconceptions
Context
Natural Language is a source of uncertainty
Concepts have different meanings to different people
Uncertitude, Misunderstandings and Misconceptions
Problems

4. Noise, Dimensionality, Computation time
Context
To make subjective judjment, a concept model must be developed for representing human natural language
Noise, Dimensionality, Computation time
Problems

5. Solution
For dealing with concepts lexicalized in natural language, Interval type-2 Fuzzy sets are used
Solution
Using Interval type-2 fuzzy logic

6. Solution
For a good knowledge interpretation and a subjective knowledge, a pre- processing step must be done in order to eliminate redundant or uncertain attribute
Solution
Using Fuzzy Feature Selection

7. Solution
Using a technique which analyses data in terms of concepts and the relation between them for a good linguistic representation
Solution
Using Fuzzy Formal concept

8. Fuzzy Formal Concept: FFC
Is an extension of the Formal Concept Analysis FCA
The FFC is a form of knowledge representation used when the FCA has fuzzy attributes.
The relation is illustrated via a fuzzy concept lattice for representing objects and attributes connections.
Almost all the methods implementing FFC, use Type-1 Fuzzy Logic for representing relations between objects and attributes.

9. IT-2 FFC : Interval type-2 Fuzzy Formal Concept
Proposed Approach
IT-2 FFC : Interval type-2 Fuzzy Formal Concept
Fuzzy Formal Concept
IT-2 Feature Selection

10. Fuzzy Feature Selection Feature Selection = Attribute Reduction
In order to symplify models, decrease time complexity, facilitate data visualization and make decision, a feature selection is done
For analysing vague and uncertain data, Interval type -2 Fuzzy Feature selection is used
Feature Selection = Attribute Reduction

11. Proposed Approach
The Feature Selection is done by computing the centroid of each FOU
Only centroid with high values are kept
The others (with low centroids) correspond to irrelevant features
The centroids are necessary to implement the reduction step from type-2 fuzzy feature selection to type-1 one

12. Non Significant Feature
Proposed Approach
Significant Feature
Yes
Centroids computed
>
Threshold No
Non Significant Feature

13. Proposed architecture

14. Experimental Results
The Mendel Data set is used: 28 subjects, 32 features
For each concept , a FOU is generated

15. Experimental Results
Only centroids > 7 are kept  from 32 concepts, only 13 concepts will be kept

16. Experimental Results
The FFC before IT-2 feature selection

17. Experimental Results
The FFC after IT-2 feature selection

18. Conclusion and Perspectives
Fuzzy logic especially IT-2 FSs is a very powerful tool for representing concepts
Features selection consist on keeping only relevant information for better interpretation
dimentionality is reduced Fuzzy Formal Lattice can be generated and well interpreted
As future works, testing the approach on some real world data.

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