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

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

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

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

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

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

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

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.

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

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

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

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

Proposed architecture

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

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

Experimental Results The FFC before IT-2 feature selection

Experimental Results The FFC after IT-2 feature selection

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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