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Imprecise Categories, Approximation, and Rough Sets Chapter 2
Review   Classification Equivalence relations A category in R containing an element x   U   [x] R   The family of all equivalence classes of R( or classification of U) referred to as categories of R U/ R  X is a concept / category in U A family of concepts in U will be referred to as abstract knowledge about U   X    U
Contents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Theories of  fuzzy   sets  and  rough   sets  are  generalizations  of classical set theory for modeling  vagueness and uncertainty
[object Object],[object Object],[object Object],[object Object],[object Object],We will use rough set notion here for handling the vagueness of knowledge
1.Rough Sets ,[object Object],[object Object],[object Object]
2. Lower and Upper Approximations ,[object Object],[object Object],[object Object],[object Object]
Take a closer look! The universe of discourse is the finite set of all objects under consideration. The attribute (equivalence relation) R 1  divides the universe of discourse into a set of equivalence classes (elementary categories) as shown. Classification using R1 Classification using R2 The attribute (equivalence relation) R 2  divides the universe of discourse into a set of equivalence classes (elementary categories) as shown. Applying the family of attributes (equivalence relations) R simultaneously divides the universe of discourse into a set of basic categories as shown. Classification using R={R1, R2}
Take a closer look! A set that can not be precisely determined using the available knowledge is called a Rough Set. Our goal is to use the concepts of Rough Set theory to approximately determine the set using available knowledge.  The set  R X is the set of  all elements of U which can be certainty classified as elements of X in the Knowledge R Lower approximation of X: x     R  X if and only if  [x] R    X Lower Approximation of X
Take a closer look! ,[object Object],[object Object],The set  is the set of elements of U which can be possibly classified as elements of X, in employing knowledge R Upper Approximation of X
Take a closer look!
Take a closer look! The Negative Region of X
Still U can’t understand?! ∴  X  is  R -rough (undefinable) U U/R R :  subset  of   attributes set   X ∴  X  is  R -definable U/R U set  X X  is  R-definable  (or crisp)  if and only if  ( i.e  X  is the union of some  R -basic categories,  called  R-definable set ,  R-exact set ) X  is  R-undefinable  ( rough )  with respect to  R  if and only if  ( called  R - inexact ,  R - rough ) is the maximal   R-definable   set contained in X is the minimal   R-definable   set containing X
Example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],yes 3 1 6 d c a U 7 5 4 3 2 1 no 3 3 no 3 3 Yes 2 2 no 2 2 no 1 1 yes 4 1
yes yes/no no {x1,   x6} {x3,   x4 } {x2, x5,x7}
4.Properties of Approximations
4.Properties of Approximations Cont’
5.Approximations and Membership Relation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object]
Propositions
6.Numerical Characterization of Imprecision ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example ,[object Object],yes 3 1 6 d c a U 7 5 4 3 2 1 no 3 3 no 3 3 Yes 2 2 no 2 2 no 1 1 yes 4 1
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],yes 3 1 6 d c a U 7 5 4 3 2 1 no 3 3 no 3 3 Yes 2 2 no 2 2 no 1 1 yes 4 1
7.  Topological Characterization of Imprecision ,[object Object]
Take a closer look! Universe with Classification 1 U|R Universe with Classification 2 U|R
Take a closer look! R X X Roughly R-definable
Take a closer look! X Internally R-undefinable
Take a closer look! R X X Externally R-undefinable
Take a closer look! X Totally R-undefinable
8.Approximation of Classifications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],yes 3 1 6 d c a U 7 5 4 3 2 1 no 3 3 no 3 3 Yes 2 2 no 2 2 no 1 1 yes 4 1
[object Object],[object Object],[object Object],[object Object]
Example ,[object Object],[object Object]
9.Rough Equality of Sets ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
Propositions
Proposition
 

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Imprecise Categories Approximation Rough Sets Chapter Review

  • 1. Imprecise Categories, Approximation, and Rough Sets Chapter 2
  • 2. Review Classification Equivalence relations A category in R containing an element x  U [x] R The family of all equivalence classes of R( or classification of U) referred to as categories of R U/ R X is a concept / category in U A family of concepts in U will be referred to as abstract knowledge about U X  U
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9. Take a closer look! The universe of discourse is the finite set of all objects under consideration. The attribute (equivalence relation) R 1 divides the universe of discourse into a set of equivalence classes (elementary categories) as shown. Classification using R1 Classification using R2 The attribute (equivalence relation) R 2 divides the universe of discourse into a set of equivalence classes (elementary categories) as shown. Applying the family of attributes (equivalence relations) R simultaneously divides the universe of discourse into a set of basic categories as shown. Classification using R={R1, R2}
  • 10. Take a closer look! A set that can not be precisely determined using the available knowledge is called a Rough Set. Our goal is to use the concepts of Rough Set theory to approximately determine the set using available knowledge. The set R X is the set of all elements of U which can be certainty classified as elements of X in the Knowledge R Lower approximation of X: x  R X if and only if [x] R  X Lower Approximation of X
  • 11.
  • 12. Take a closer look!
  • 13. Take a closer look! The Negative Region of X
  • 14. Still U can’t understand?! ∴ X is R -rough (undefinable) U U/R R : subset of attributes set X ∴ X is R -definable U/R U set X X is R-definable (or crisp) if and only if ( i.e X is the union of some R -basic categories, called R-definable set , R-exact set ) X is R-undefinable ( rough ) with respect to R if and only if ( called R - inexact , R - rough ) is the maximal R-definable set contained in X is the minimal R-definable set containing X
  • 15.
  • 16. yes yes/no no {x1, x6} {x3, x4 } {x2, x5,x7}
  • 19.
  • 20.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26. Take a closer look! Universe with Classification 1 U|R Universe with Classification 2 U|R
  • 27. Take a closer look! R X X Roughly R-definable
  • 28. Take a closer look! X Internally R-undefinable
  • 29. Take a closer look! R X X Externally R-undefinable
  • 30. Take a closer look! X Totally R-undefinable
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.  
  • 38.