1 / 10

3. Rough set extensions

3. Rough set extensions In the rough set literature, several extensions have been developed that attempt to handle better the uncertainty present in real world data.

kueng
Download Presentation

3. Rough set extensions

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 3. Rough set extensions • In the rough set literature, several extensions have been developed that attempt to handle better the uncertainty present in real world data. • Variable precision rough set is generalized model of rough sets, allowing a controlled degree of misclassification by relaxing the subset operator. • Fuzzy rough sets and tolerance rough sets handle real-valued data by replacing the traditional equivalence classes of crisp rough set theory with alternatives that are better suited to dealing with this type of data. • 3.1 Variable precision rough sets • Variable precision rough sets (VPRS) attempts to improve upon rough set theory by relaxing the subset operator. It was proposed to analyze and identify data patterns which represent statistical trends rather than functional. ISA Lab., CU, Korea

  2. The main idea of VPRS is to allow objects to be classified with an error smaller than a certain predefined level. • This approach is arguably easiest to be understood within the framework of classification. Let X, Y  U, the relative classification error is defined by • Observe that c(X, Y)=0 if and only if XY. A degree of inclusion can be achieved by allowing a certain level of error, , in classification: • Using  instead of , the -upper and -lower approximations of a set X can be defined as • Note that for =0. ISA Lab., CU, Korea

  3. The positive, negative and boundary regions in the original rough set theory can now be extended to • In the example dataset in Table 1, (15) can be used to calculate the -positive region for R={b, c}, X={e} and =0.4. • U/R={{2}, {0, 4}, {3}, {1,6,7}, {5}} • U/X={{0}, {1, 3, 6}, {2, 4, 5, 7}} • For each set AU/R and BU/X, the value of c(A,B) must be less than  if the equivalence class A is to be included in the -positive region. Considering A={2} gives • c({2}, {0})=1>  • c({2}, {1, 3, 6})=1>  • c({2}, {2, 4, 5, 7})=0<  ISA Lab., CU, Korea

  4. So object 2 is added to the -positive region as it is a -subset of {2, 4, 5, 7} • Taking A={1, 6, 7}, a more interesting case is encountered: • c({1, 6, 7}, {0})=1> • c({1, 6, 7}, {1, 3, 6})=0.3333< • c({1, 6, 7}, {2, 4, 5, 7})=0.6667> • Here the objects 1, 6 and 7 included in the -positive region as the set {1, 6, 7} is a -subset of {1, 3, 6}. • Calculating the subsets in this way leads to the following -positive region: • Compare this with the positive region generated previously: {2, 3, 5}. Objects 1, 6 and 7 are now included due to the relaxation of the subset operator. • Consider a decision table (U, CD), where C is the set of conditional attributes and D is the set of decision attributes. The -positive region of an equivalence relation Q on U may be determined by ISA Lab., CU, Korea

  5. where R is also an equivalence relation on U. This can then be used to calculate dependencies and thus determine -reducts. This dependency function become • 3.2 Tolerance rough sets • Another way of attempting to handle imprecision is to introduce a measure of similarity of attribute values and define the lower and upper approximations based on these similarity measures. • 3.2.1 Similarity measures • In this approach, suitable similarity relations must be defined for each attribute, although the same definition can be used for all attributes if applicable. A standard measure for this purpose is • where a is the attribute under consideration, and amax and amin denote the maximum and minimum values respectively for this attribute. ISA Lab., CU, Korea

  6. When considering more than one attribute, the defined similarities must be combined to provide a measure of the overall similarity of objects. For a subset of attributes, P, this can be achieved in many ways; two commonly adopted approaches are • where  is a global similarity threshold. • From this, the so-called tolerance classes that are generated by a given similarity relation for an object x are defined as ISA Lab., CU, Korea

  7. 3.3.2 Approximations and dependency • Lower and upper approximations are then defined in a similar way to traditional rough set theory: • The tuple is called a tolerance rough set. • Positive region and dependency functions then become ISA Lab., CU, Korea

  8. 3.3 Fuzzy-rough sets • 3.3.1 Main approaches • In the same way that crisp equivalence classes are central to rough sets, fuzzy equivalence classes are central to the fuzzy-rough set approach. • The concept of crisp equivalence classes can be extended by the inclusion of a fuzzy similarity relation S on the universe, which determines the extent to which two elements are similar in S. • The usual properties of reflexivity (S(X, Y)=1), symmetry (S(x, y)=S(y, x)) and transitivity (S(x, z)S(x, y)S(y, z), where  is a t-norm) hold. • Using such a fuzzy similarity relation S, the fuzzy equivalence class [x]S for the objects close to x can be defined: • [Def. 3.1] Suppose FS=(U, A, V, f) is a knowledge representation system, and I is defined as an equivalence relation on U, while FX is a fuzzy set on U, PA, FXU, then a pair of the lower approximation and the upper approximation • of the knowledge representation system FS about FX is described, respectively, as ISA Lab., CU, Korea

  9. Where FX(x) is the membership degree that x belongs to FX, and the upper approximation and the lower approximation of the fuzzy set are still fuzzy sets. Could be understood as the membership degree that the object definitely belongs to the fuzzy set FX, while could be understood as the membership degree that the object possibly belongs to the fuzzy set. • [Ex. 5.1] Suppose U={n1, n2, n3, n4, n5, n6, n7, n8} is a group consisted of eight students studied. They are divided into three equivalent classes, denoted by U/I={N, M}, U/I={{n1, n6}, {n2, n7, n8}, {n3, n4, n5}}. Suppose the fuzzy set FX expresses the fuzzy concept “height,” and its membership function is FX(X)={n1/0.6, n2/0.4, n3/0.4, n4/0.7, n5/0.6, n6/0.8, n7/1, n8/0.9}, calculates the upper approximation, the lower approximation, and the classification accuracy of the rough fuzzy set FX. • [Solution] ISA Lab., CU, Korea

  10. [Def. 5.2] FS=(U, R) is a fuzzy approximation space, Where U is a nonempty universe, and R is an approximate relation on U, xU, yU, PA, FXU, the input class F1, F2, …, Fn is a fuzzy cluster originated from a fuzzy equivalence relation, and n is the number of the cluster. Every Fi is a fuzzy equivalent class. Thus, a pair of the upper approximation and the lower approximation of the fuzzy rough set FX on U is • Obviously, when the equivalence relation is clear, a fuzzy rough set will degrade into a rough fuzzy set. When all fuzzy equivalence relations are clear, it will further degrade into a classical rough set. ISA Lab., CU, Korea

More Related