An Improved Value Reduction Algorithm Based on Core Value

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.187.216 ◽

2011 ◽

Vol 187 ◽

pp. 216-220

Author(s):

Wei Du ◽

Wei Wang

Keyword(s):

Information System ◽

Decision Rule ◽

Reduction Algorithm ◽

Core Value ◽

Conditional Attribute ◽

Improved Algorithm ◽

Attribute Value

Value reduction algorithm can filter and delete redundant conditional attribute value, so as to obtain decision rule of information system with least conditional attribute values. Based on the introduction of basic value reduction algorithm, the paper supplemented functions. Aiming at the circumstance of there is no repeated record and no conflict after deleting some attributes of a record, the algorithm supplement it. The example of value reduction based on the improved algorithm illustrated that it is an effective value reduction algorithm and an important supplement of basic value reduction algorithm.

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Three-way decision for incomplete real-valued data

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201272 ◽

2020 ◽

Vol 39 (5) ◽

pp. 7843-7862

Author(s):

Haili Wen ◽

Fei Xia ◽

Hongxiang Tang

Keyword(s):

Information System ◽

Decision Rule ◽

Rough Set ◽

Equivalence Relation ◽

Risk Preference ◽

Decision Makers ◽

Gaussian Kernel ◽

Decision Attributes ◽

Different Levels ◽

Conditional Attribute

An information system (IS) is a database that expresses relationships between objects and attributes. An IS with decision attributes is said to be a decision information system (DIS). An incomplete real-valued decision information system (IRVDIS) is a DIS based on incomplete real-valued data. This paper studies three-way decision (3WD) for incomplete real-valued data and its application. In the first place, the distance between two objects on the basis of the conditional attribute set in an IRVDIS is constructed. In the next place, the fuzzy Tcos-equivalence relation on the object set of an IRVDIS is received by means of Gaussian kernel. After that, the decision-theoretic rough set model for an IRVDIS is presented. Furthermore, the 3WD method is proposed based on this model. Lastly, to illustrate the feasibility of the proposed method, an application of the proposed method is given. It is worth mentioning that levels of risk may be determined by thresholds that can be directly acquired according to risk preference of different decision-makers, as well as the decision rule for each decision class under different levels of risk is showed in tabular forms.

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Attribute Reduction Algorithm for Information System without Decision Attributes

Proceedings of the 2nd International Conference on Electronic and Mechanical Engineering and Information Technology (2012) ◽

10.2991/emeit.2012.32 ◽

2012 ◽

Cited By ~ 1

Author(s):

Hong-Chan Li ◽

Hao-Dong Zhu

Keyword(s):

Information System ◽

Attribute Reduction ◽

Reduction Algorithm ◽

Decision Attributes

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Intelligent Query Answering

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch121 ◽

2011 ◽

pp. 639-643 ◽

Cited By ~ 2

Author(s):

Zbigniew W. Ras ◽

Agnieszka Dardzinska

Keyword(s):

Information System ◽

Hierarchical Structure ◽

Cooperative Systems ◽

Query Answering ◽

Target Information ◽

Attribute Value

One way to make query answering system (QAS) intelligent is to assume a hierarchical structure of its attributes. Such systems have been investigated by Cuppens & Demolombe (1988), Gal & Minker (1988), and Gaasterland et al. (1992), and they are called cooperative. Any attribute value listed in a query, submitted to cooperative QAS, is seen as a node of the tree representing that attribute. If QAS retrieves an empty set of objects, which match query q in a target information system S, then any attribute value listed in q can be generalized and the same the number of objects that possibly can match q in S can increase. In cooperative systems, these generalizations are usually controlled by users.

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An attribute reduction algorithm in the incomplete information system based on the attribute significance

2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA) ◽

10.1109/wartia.2014.6976546 ◽

2014 ◽

Author(s):

Chen Zhen ◽

Xing Xiao Xue

Keyword(s):

Information System ◽

Incomplete Information ◽

Attribute Reduction ◽

Reduction Algorithm ◽

Incomplete Information System

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An improved attribute reduction algorithm based on importance of attribute value

Procedia Engineering ◽

10.1016/j.proeng.2010.11.057 ◽

2010 ◽

Vol 7 ◽

pp. 356-359 ◽

Cited By ~ 1

Author(s):

Ju Li ◽

Xiaowen Fan ◽

Xing Wang

Keyword(s):

Attribute Reduction ◽

Reduction Algorithm ◽

Attribute Value

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Calculation of Semantic Similarity Property Based on Generalized Semantic Weak Incomplete Information System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1049-1050.1440 ◽

2014 ◽

Vol 1049-1050 ◽

pp. 1440-1443

Author(s):

Xue Ding ◽

Hong Hong Yang ◽

Ren Zhang

Keyword(s):

Information System ◽

Incomplete Information ◽

Semantic Similarity ◽

Property Values ◽

Incomplete Information System ◽

Property Type ◽

Similarity Calculation ◽

Degree Of Similarity ◽

Attribute Value

Each object has its own specific properties, objects can be uniquely identified by its properties. "Properties" are properties of all the main features of the concept only, property values are often given a certain amount of semantics, in the calculation of similarity among the different attributes if only to consider the type of calculation is obviously not complete [1]. For example: blue and blue, the similarity calculation in the property type, we can not determine its degree of similarity, but it is the same type of semantic expression under the different languages. Another example: domperidone and domperidone, which is the same type of drugs. Therefore, we attribute value in the calculation of the time, but also taking into account the semantic similarity.

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