Incomplete Information Tables and Rough Classification

Stefanowski, Jerzy; Tsoukiàs, Alexis

doi:10.1111/0824-7935.00162

Cited by 317 publications

(140 citation statements)

References 14 publications

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“…To test and compare the performances of the MRPR algorithms and traditional heuristic attribute reduction algorithms based on positive region (RPR) [17] and A general improved feature selection algorithm based on the positive region (FSPR) [18], we download six data sets from UCI [21]. All these data sets are outlined in Table 3.…”

Section: Algorithm a Structure Discernibility Matrix Reduction Algormentioning

confidence: 99%

Attribute Reduction Algorithm Based on Structure Discernibility Matrix in Composite Information Systems

Fan

Sun

2017

ITM Web Conf.

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Abstract. Attribute reduction, as an important preprocessing step for knowledge acquiring in data mining, is one of the key issues in rough set theory. It can only deal with attributes of a specific type in the information system by using a specific binary relation. However, there may be attributes of multiple different types in information systems in real-life applications. A composite relation is proposed to process attributes of multiple different types simultaneously in composite information systems. In order to solve the time-consuming problem of traditional heuristic attribute reduction algorithms, a novel attribute reduction algorithm based on structure discernibility matrix was proposed in this paper. The proposed algorithms can choose the same attribute reduction as its previous version, but it can be used to accelerate a heuristic process of attribute reduction by avoiding the process of intersection and adopting the forward greedy attribute reduction approach. The theoretical analysis and experimental results with UCI data sets show that the proposed algorithm can accelerate the heuristic process of attribute reduction.

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Section: Algorithm a Structure Discernibility Matrix Reduction Algormentioning

confidence: 99%

Attribute Reduction Algorithm Based on Structure Discernibility Matrix in Composite Information Systems

Fan

Sun

2017

ITM Web Conf.

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“…Decision theoretic approaches have also been used in order to induce classification rules from (partially) inconsistent and/or incomplete data bases [333]. For other examples, see [400,401,402,403]. How can we take advantage of new procedures for combination to build better learning algorithms?…”

Section: Large Databases and Inferencementioning

confidence: 99%

Computer science and decision theory

Roberts

2008

Ann Oper Res

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This paper reviews applications in computer science that decision theorists have addressed for years, discusses the requirements posed by these applications that place great strain on decision theory/social science methods, and explores applications in the social and decision sciences of newer decision-theoretic methods developed with computer science applications in mind. The paper deals with the relation between computer science and decision-theoretic methods of consensus, with the relation between computer science and game theory and decisions, and with "algorithmic decision theory."

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“…The most convenient way to define the characteristic relation is through the characteristic sets. For decision tables, in which all missing attribute values are lost, a special characteristic relation was defined in [18], see also, e.g., [17,19].…”

Section: Blocks Of Attribute-value Pairsmentioning

confidence: 99%

“…Recently rough set theory was extended to handle incomplete data sets (with missing attribute values) [1][2][3][4][5][6][7][8][9][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Local and Global Approximations for Incomplete Data

Grzymała-Busse

Rząsa

2006

Lecture Notes in Computer Science

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Abstract. For completely specified decision tables, where lower and upper approximations are unique, the lower approximation is the largest definable set contained in the approximated set X and the upper approximation of X is the smallest definable set containing X. For incomplete decision tables the existing definitions of upper approximations provide sets that, in general, are not minimal definable sets. The same is true for approximations based on relations that are generalizations of the equivalence relation. In this paper we introduce two definitions of approximations, local and global, such that the corresponding upper approximations are minimal. Local approximations are more precise than global approximations. Global lower approximations may be determined by a polynomial algorithm. However, algorithms to find both local approximations and global upper approximations are NP-hard.

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Incomplete Information Tables and Rough Classification

Cited by 317 publications

References 14 publications

Attribute Reduction Algorithm Based on Structure Discernibility Matrix in Composite Information Systems

Attribute Reduction Algorithm Based on Structure Discernibility Matrix in Composite Information Systems

Computer science and decision theory

Local and Global Approximations for Incomplete Data

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