Optimistic concept-based incomplete decision information system rule extraction algorithm

A technology for decision-making information and concepts, applied in computing, computing models, knowledge expression, etc., can solve the problems of redundant decision-making rules, lack of knowledge acquisition, and reduce the traditional concept lattice rule extraction algorithm to reduce complexity and redundancy. The effect of the judgment of the remainder rule

Pending Publication Date: 2019-12-17
TAIYUAN UNIV OF TECH
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Problems solved by technology

However, the process of obtaining the concept of compatibility is cumbersome, and there is redundancy in the obtained decision rules
Wei Ling and others studied the problem of approximate concept acquisition based on k-order relations, and analyzed their relationship with formal concepts, attribute-oriented concepts, and object-oriented concepts, but did not carry out knowledge acquisition.
[0004] FCA and RST have the same research background and goals, so there must be some essential connections. Wei Ling discussed the relationship between FCA and the power set of equivalence classes in RST, and gave the concepts of constructing concept lattices by partitioning and obtaining concepts from concept lattices. The specific method of division; Kang proposed a rough set theoretical model based on the FCA theory,

Method used

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  • Optimistic concept-based incomplete decision information system rule extraction algorithm
  • Optimistic concept-based incomplete decision information system rule extraction algorithm
  • Optimistic concept-based incomplete decision information system rule extraction algorithm

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Embodiment 1

[0072] As shown in Table 1, it is an incomplete decision information system T={U,A,V,f},

[0073] Where U={1,2,3,4,5,6}, A={P,M,S,X,D}, where P represents the attribute Price, M represents the attribute Mileage, S represents the attribute Size, and X represents the attribute Max_speed and D are decision attributes.

[0074] Table 1. Incomplete multivalued background

[0075]

[0076] First, obtain all compatible classes under the decision attributes as: U / D={{1,2,4,6},{3},{5}}.

[0077] In the case of granularity ω=1, the coverage of U under the conditional attributes is obtained, all compatible classes are obtained, and the corresponding optimistic concept is obtained, as shown in Table 2:

[0078] Table 2. Optimistic concept calculation process when ω=1

[0079]

[0080] To judge whether the extension of all optimistic concepts is a subset of the decision compatible class, we can get: only the concept (3, S) satisfies the condition, and a decision rule can be obtained:

[0081] In the ...

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Abstract

The invention discloses an optimistic concept-based incomplete decision information system rule extraction algorithm, provides an optimistic concept lattice based on a compatible relationship for thefirst time for the rule extraction algorithm of the incomplete decision information system, and provides a corresponding rule extraction algorithm of the incomplete decision information system on thebasis of the optimistic concept lattice. According to the algorithm, a granularity concept is introduced; the algorithm has the following steps: solving an optimistic concept of each layer in a granularity space from coarse to fine; and obtaining the simplest rule in the decision information system according to the relationship between the optimistic concept and the decision attribute in the decision information system, setting whether the discourse domain element of the extracted rule covers the whole discourse domain as an algorithm termination condition, and finally realizing the rule extraction process of the decision information system. Theorem proves and instance analysis can explain correctness and effectiveness of the new algorithm, and effectiveness and rapidity of the algorithm are verified through experiments.

Description

Technical field [0001] The incomplete decision information system rule extraction algorithm based on the optimistic concept belongs to the technical field of decision information system rule extraction. Background technique [0002] Information system is the main research object of machine learning, and decision information system is an important manifestation of information system. The rule extraction of decision information system is one of the research contents of data analysis. Rough set theory (rough set theory, RST) is an effective mathematical tool for processing data. In recent years, the use of rough set to extract the rules of decision information system has attracted great attention. The research of most scholars. [0003] In 1982, Wille proposed the concept lattice theory, which is a mathematical tool to describe incomplete and uncertain information. It can effectively analyze various incomplete information such as inaccuracy, inconsistency and incompleteness. Using c...

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Application Information

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IPC IPC(8): G06N5/02G06N20/00
CPCG06N5/025G06N20/00
Inventor 陈泽华闫心怡柴晶赵哲峰刘晓峰刘帆
Owner TAIYUAN UNIV OF TECH
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