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1
Content available remote Attribute Reduction Using Extension of Covering Approximation Space
100%
Wang G. Y. , Hu J.
Fundamenta Informaticae
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2012
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tom Vol. 115, nr 2/3
219-232
EN
The concept of the complement of a covering is introduced, and then the extended space of a covering approximation space is induced based on it. Generally, the extended space of a covering approximation space generates a bigger covering lower approximation or smaller covering upper approximation than itself. Through extending each covering of a covering decision system, the classification ability of each covering may be improved. Thus, a heuristic reduction algorithm is developed to eliminate some coverings in a covering decision system without decreasing the classification ability of the system for decision. Theoretical analysis and experimental results indicate that this algorithm can often get smaller reduction than other algorithms.
2
Content available remote Reducts and Constructs in Attribute Reduction
100%
Susmaga R.
Fundamenta Informaticae
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2004
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tom Vol. 61, nr 2
159--181
EN
One of the main notions in the Rough Sets Theory (RST) is that of a reduct. According to its classic definition, the reduct is a minimal subset of the attributes that retains some important properties of the whole set of attributes. The idea of the reduct proved to be interesting enough to inspire a great deal of research and resulted in introducing various reduct-related ideas and notions. First of all, depending on the character of the attributes involved in the analysis, so called absolute and relative reducts can be defined. The more interesting of these, relative reducts, are minimal subsets of attributes that retain discernibility between objects belonging to different classes. This paper focuses on the topological aspects of such reducts, identifying some of their limitations and introducing alternative definitions that do not suffer from these limitations. The modified subsets of attributes, referred to as constructs, are intended to assist the subsequent inductive process of data generalisation and knowledge acquisition, which, in the context of RST, usually takes the form of decision rule generation. Usefulness of both reducts and constructs in this role is examined and evaluated in a massive computational experiment, which was carried out for a collection of real-life data sets.
3
Content available remote Attribute Reduction in Formal Contexts: A Covering Rough Set Approach
88%
Li T.-J. , Wu W. Z.
Fundamenta Informaticae
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2011
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tom Vol. 111, nr 1
15-32
EN
This paper proposes an approach to attribute reduction in formal contexts via a covering rough set theory. The notions of reducible attributes and irreducible attributes of a formal context are first introduced and their properties are examined. Judgment theorems for determining all attribute reducts in the formal context are then obtained. According to the attribute reducts, all attributes of the formal context are further classified into three types and the characteristic of each type is characterized by the properties of irreducible classes of the formal context. Finally, by using the discernibility attribute sets, a method of distinguishing the reducible attributes and the irreducible attributes in formal contexts is presented.
4
Content available remote Vector-based Attribute Reduction Method for Formal Contexts
88%
Shao M. , Liu M. , Guo L.
Fundamenta Informaticae
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2013
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tom Vol. 126, nr 4
397--414
EN
Attribute reduction is one basic issue in knowledge discovery of information systems. In this paper, based on the object oriented concept lattice and classical concept lattice, the approach of attribute reduction for formal contexts is investigated. We consider attribute reduction and attribute characteristics from the perspective of linear dependence of vectors. We first introduce the notion of context matrix and the operations of corresponding column vectors, then present some judgment theorems of attribute reduction for formal contexts. Furthermore, we propose a new method to reducing formal context and show corresponding reduction algorithms. Compared with previous reduction approaches which employ discernibility matrix and discernibility function to determine all reducts, the proposed approach is more simpler and easier to implement.
5
Content available remote Comparative Study of Ordered and Covering Information Systems
88%
Wang C. , He Q. , Chen D. , Hu Q.
Fundamenta Informaticae
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2013
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tom Vol. 123, nr 3
351--363
EN
Covering information systems and ordered information systems are two important types of information systems. In this paper the relationships between covering information systems and ordered information systems are first examined, and it is proved that these two types of information systems are isomorphic under given conditions and can be equivalently transformed into each other. Then, the approach to attribute reduction in ordered information systems is proposed. Based on the isomorphism and equivalence of transformation, the method of attribute reduction in a covering information system can be directly obtained according to the reduction approach in an ordered in- formation system. A practical example is employed to show that the proposed method is an effective technique to deal with complex data sets.
6
Content available remote A Comparison of Rough Set Methods and Representative Inductive Learning Algorithms
88%
Miao D. , Hou L.
Fundamenta Informaticae
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2004
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tom Vol. 59, nr 2,3
203--219
EN
Rough set theory is a kind of new tool to deal with knowledge, particularly when knowledge is imprecise, inconsistent and incomplete. In this paper, we discuss some inductive machine learning techniques in the framework of the knowledge reduction approach based on rough set theory. The Monk's problems introduced in the early of nineties are resolved again employing rough set methods and their results are compared and analyzed with those obtained at that time. As far as accuracy and conciseness are concerned, the learning algorithms based on rough sets have remarkable superiority.
7
Content available remote Non-Monotonic Attribute Reduction in Decision-Theoretic Rough Sets
88%
Li H. , Zhou X. , Zhao J. , Liu D.
Fundamenta Informaticae
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2013
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tom Vol. 126, nr 4
415--432
EN
For most attribute reduction in Pawlak rough set model (PRS), monotonicity is a basic property for the quantitative measure of an attribute set. Based on the monotonicity, a series of attribute reductions in Pawlak rough set model such as positive-region-preserved reductions and condition entropy-preserved reductions are defined and the corresponding heuristic algorithms are proposed in previous rough sets research. However, some quantitative measures of attribute set may be non-monotonic in probabilistic rough set model such as decision-theoretic rough set (DTRS), and the non-monotonic definition of the attribute reduction should be reinvestigated and the heuristic algorithm should be reconsidered. In this paper, the monotonicity of the positive region in PRS and DTRS are comparatively discussed. Theoretic analysis shows that the positive region in DTRS model may be expanded with the decrease of the attributes, which is essentially different from that in PRS model. Hereby, a new non-monotonic attribute reduction is presented for the DTRS model in this paper, and a heuristic algorithm for searching the newly defined attribute reduction is proposed, in which the positive region is allowed to be expanded instead of remaining unchanged in the process of attribute reduction. Experimental analysis is included to validate the theoretic analysis and quantify the effectiveness of the proposed attribute reduction algorithm.
8
Content available remote A Comparative Study of Algebra Viewpoint and Information Viewpoint in Attribute Reduction
75%
Wang G.-Y. , Zhao J. , An J. , Wu Y.
Fundamenta Informaticae
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2005
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tom Vol. 68, nr 3
289--301
EN
Attribute reduction is an important issue in rough set theory and has already been studied from the algebra viewpoint and information viewpoint of rough set theory respectively. However, the concepts of attribute reduction based on these two different viewpoints are not equivalent to each other. In this paper, we make a comparative study on the quantitative relationship between some basic concepts of rough set theory like attribute reduction, attribute significance and core defined from these two viewpoints. The results show that the relationship between these conceptions from the two viewpoints is rather an inclusion than an equivalence due to the fact that the rough set theory discussed from the information point of view restricts attributes and decision tables more specifically than it does when considered from the algebra point of view. The identity of the two viewpoints will hold in consistent information decision tables only. That is, the algebra viewpoint and information viewpoint are equivalent for a consistent decision table, while different for an inconsistent decision table. The results are significant for the design and development of methods for information reduction.
9
Content available Features Reduction Using Logic Minimization Techniques
75%
Borowik G. , Łuba T. , Zydek D.
International Journal of Electronics and Telecommunications
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2012
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tom Vol, 58, No. 1
71-76
EN
This paper is dedicated to two seemingly different problems. The first one concerns information theory and the second one is connected to logic synthesis methods. The reason why these issues are considered together is the important task of the efficient representation of data in information systems and as well as in logic systems. An efficient algorithm to solve the task of attributes/arguments reduction is presented.
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