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Numerical characterizations of covering rough sets based on evidence theory

Numerical characterizations of covering rough sets based on evidence theory. Chen Degang, Zhang Xiao Department of Mathematics and Physics, North China Electric Power University, Beijing, 102206, P. R. China.

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Numerical characterizations of covering rough sets based on evidence theory

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  1. Numerical characterizations of covering rough sets based on evidence theory Chen Degang, Zhang Xiao Department of Mathematics and Physics, North China Electric Power University, Beijing, 102206, P. R. China

  2. Outline:1. Introduction 2. Basic notions related to covering rough sets 3. Belief function and plausibility function of covering rough sets 4. Numerical characterizations of attribute reduction of covering information system 5. Numerical characterizations of attribute reduction of covering decision systems 6. Conclusions

  3. 1. Introduction What is covering rough sets? Coveringrough sets are improvements of traditional rough sets by considering cover of universe instead of partition. Why do we need covering rough sets? Partition and equivalence relation are too restrictive to many applications. One response to this argument is to extend an equivalence relation to more general relations such as similarity relation [21], tolerance relation [4,20] or others [22,25,26]. Another response is to consider a cover instead of partition and obtain covering rough sets [1-3,5,9,16,28,29-32].

  4. 1. Introduction The existing study on covering rough sets Zakowski employed coverings of universe to establish the generalized rough sets [28]. Bonikowski et al. [1] studied the structures of covers. Mordeson [9] examined the relationship between the approximations of sets defined with respect to covers and some axioms satisfied by traditional rough sets. Chen et al. [5] discussed the covering rough set under the framework of a complete completely distributive lattice. Zhu and Wang [29-32] compared three kinds of generalized rough sets to deal with vagueness and granularity in information system. Chen et al.[6] began to develop definition and methods of attribute reduction with covering rough sets. In [6] the intersection of coverings was defined and the discernibility matrix was employed to compute all reducts. Their study established a theoretical foundation for attribute reduction of covering decision systems.

  5. 1. Introduction Two facts motivate our idea Among these work on covering rough sets, less effort has been concentrated on developing measures for covering rough sets up to now. As well known, in traditional rough set theory different kinds of measures are proposed to reveal numerical characterizations of rough sets and applied to develop algorithms of finding reducts. This fact motivates our idea in this paper to develop measures to characterize covering rough sets numerically. As pointed in [23,24], there is closed connection between rough set theory and evidence theory. This connection further motivates us to set up connection between covering rough sets and evidence theory, i.e., to characterize approximations and attribute reductions in covering rough sets by employing measures in evidence theory.

  6. We recall the basic concepts related to covering rough sets [6]. Definition 2.1. Let be a universe, and a family of subsets of . is called a covering of if none elements in is empty and . Definition 2.2. Let be a covering of . For every , let , then is also a covering of . We call it the induced covering of . Definition 2.3.Let be a family of coverings of . For every , let , then is also a covering of . We call it the induced covering of . . For every , the lower and the upper approximations of with respect to are defined as follows The positive region . 2. Basic notions related to covering rough sets

  7. In this section, we first discuss the property of lower approximation and propose a new upper approximation for covering rough sets. Lemma 3.1. Let be a covering information system and and be a family of coverings of . For , we have In this paper, we define a new upper approximation of with respect tothe induced cover of as . Here is developed in terms of an induced cover, it certainly can be defined for arbitrary covering. Furthermore, we have the following conclusions. 3. Belief function and plausibility function of covering rough sets

  8. Theorem 3.2.Suppose is a family of coverings of , the covering lower approximation and upper approximation have the following properties: (1L) (Contraction) (1U) (Extension) (2) (Duality) (Duality) (3L) (Normality) (3U) (Normality) (4L) (Co-normality) (4U) (Co-normality) (5L) (Multiplication)(5U) (Addition) (6L) (Monotone) (6U) (Monotone) (7L) (Idempotency)(7U) (Idempotency) 3. Belief function and plausibility function of covering rough sets

  9. Theorem 3.3.Let be a covering information system, , for any , denote , . Then and are belief and plausibility functions on respectively, and the corresponding mass distribution is here defined as , and . 3. Belief function and plausibility function of covering rough sets

  10. Theorem 4.1. Let be a covering information system and be a family of coverings, , , , then is a reduct of iff , and for any nonempty subset , . 4. Numerical characterizations of attribute reduction of covering information system The reduct of covering information systems is the minimal subset of that preserves the induced covering .

  11. Theorem 4.2. Let be a covering information system and be a family of coverings, , , , , then is a reduct of iff , and for any nonempty subset , . 4. Numerical characterizations of attribute reduction of covering information system

  12. From Theorem 4.1 and 4.2 we conclude that the purpose of attribute reduction in covering information systems is to find a minimal subset which preserves or . In Theorem 4.3 may not hold since may not always hold. Generally we always have even is a basic granule, and this is one difference between covering rough sets and traditional rough sets since every basic granule equals to its lower and upper approximations in traditional rough sets. Now we define the significance of a covering in in a covering information system. 4. Numerical characterizations of attribute reduction of covering information system

  13. Definition 4.3. Let be a covering information system. , we define the significance of the covering by . Theorem4.4. Let be a covering information system. For every , is indispensable in in iff . 4. Numerical characterizations of attribute reduction of covering information system

  14. Definition 4.6.Let be a covering information system. , for every covering , we define the significance of the covering relative to by . Theorem 4.5. . 4. Numerical characterizations of attribute reduction of covering information system

  15. (1) let ; (2) for each , calculate (3) if for every , , then , go to step (6); (4) If , then let ; (5) if then return , else go to step (6); Algorithm 1. Acquire the core and the reduct for a covering information system.

  16. (6) let ; (7) for each , calculate ; (8) if , then ; (9) if then stop and output as a reduct , else go back to step (7). Let and , the time complexity of Algorithm 1 is . By Algorithm 1, we can acquire not only the core but also a proper reduct.

  17. Lemma 5.1. Let be a covering decision system, , then we have . Theorem 5.2. Let be a covering decision system, , , , then is a relative reduct of iff , and for any nonempty subset , . 5. Numerical characterizations of attribute reduction of covering decision systems Similar to attribute reduction of decision systems in traditional rough sets, attribute reduction of covering decision systems aims to find the minimal set of conditional attributes to preserve the positive region of decision attribute [6].

  18. Definition 5.3. Let be a covering decision system. , for every , we define the significance of the covering relative to in by Theorem 5.4. Let be a covering decision system. For every , is indispensable relative to in iff . 5. Numerical characterizations of attribute reduction of covering decision systems The covering decision systems can be divided into consistent covering decision systems and inconsistent covering decision systems[6]. Generally speaking, for covering decision systems, we only consider to find a minimal subset of to preserve the sum of belief functions of all decision classes.

  19. By Theorem 5.4 and the definition of , we have the following result. Theorem 5.5. . Definition 5.6.Let be a covering decision system, . For every but , we define the relative significance of the covering to by . 5. Numerical characterizations of attribute reduction of covering decision systems

  20. (1) let ; (2) for each , calculate ; (3) if for every , , then , go to step (6); (4) If , then ; (5) if then return , else go to step (6); Algorithm 2. Acquire the core and the reduction in a covering decision system.

  21. Let and , the time complexity of Algorithm 2 is . By Algorithm 2, we can acquire not only the core but also a proper reduct. (6) let ; (7) for each , calculate ; (8) if , then ; (9) if then stop and output as a reduct , else go back to step (7).

  22. 6. Conclusions The covering rough set theory is a generalization of traditional rough set theory characterized by covers instead of partitions. Since there is closed connection between rough set theory and evidence theory, we try to propose belief and plausibility functions to characterize covering rough sets. The relationships between attribute reduction and belief (plausibility) function are analyzed in covering information and decision systems respectively. We give the concepts of significance and relative significance of coverings to find reducts in covering information and decision systems. In addition, the relevant algorithms are designed. In a word, we develop a numerical method to find reducts by employing the belief function in covering information (decision) systems. Our future work will concentrate on developing other measures for covering rough sets based on belief and plausibility functions in this paper.

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  26. Thank you!

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