Attribute reduction and optimal decision rules acquisition for continuous valued information systems

Guan, Yanyong; Wang, Hongkai; Wang, Yun; Yang, Fang

doi:10.1016/j.ins.2009.04.017

Cited by 33 publications

(9 citation statements)

References 48 publications

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“…Discernibility matrix is an effective method in reduct construction, data representation and rough logic reasoning, and it is also useful mathematical tool in data mining, machine learning, etc. Many extended models of dicernibility matrices have been studied in recent years [23][24][25][26][27][28][29][30]. Considering the classification property ∆, Miao et al [31] constructed a general discernibility matrix…”

Section: Preliminariesmentioning

confidence: 99%

A Quick Algorithm for Binary Discernibility Matrix Simplification using Deterministic Finite Automata

Zhang

et al. 2018

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The binary discernibility matrix, originally introduced by Felix and Ushio, is a binary matrix representation for storing discernible attributes that can distinguish different objects in decision systems. It is an effective approach for feature selection, knowledge representation and uncertainty reasoning. An original binary discernibility matrix usually contains redundant objects and attributes. These redundant objects and attributes may deteriorate the performance of feature selection and knowledge acquisition. To overcome this shortcoming, row relations and column relations in a binary discernibility matrix are defined in this paper. To compare the relationships of different rows (columns) quickly, we construct deterministic finite automata for a binary discernibility matrix. On this basis, a quick algorithm for binary discernibility matrix simplification using deterministic finite automata (BDMSDFA) is proposed. We make a comparison of BDMR (an algorithm of binary discernibility matrix reduction), IBDMR (an improved algorithm of binary discernibility matrix reduction) and BDMSDFA. Finally, theoretical analyses and experimental results indicate that the algorithm of BDMSDFA is effective and efficient.

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Section: Preliminariesmentioning

confidence: 99%

A Quick Algorithm for Binary Discernibility Matrix Simplification using Deterministic Finite Automata

Zhang

et al. 2018

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“…Mutual information preservation reduction [14] can leave mutual information, with respect to the decision attributes, unchanged. In the last twenty years, many methods for attribute reduction have been studied, such as discernibility matrix-based attribute reduction methods [15][16][17][18][19][20], heuristic attribute reduction methods [10,[21][22][23], metaheuristic attribute reduction methods [24][25][26][27][28][29][30], and so on.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Approaches to Attribute Reduction for Generalized Decision Preservation

Zhang

Gao

2019

Applied Sciences

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Attribute reduction is a challenging problem in rough set theory, which has been applied in many research fields, including knowledge representation, machine learning, and artificial intelligence. The main objective of attribute reduction is to obtain a minimal attribute subset that can retain the same classification or discernibility properties as the original information system. Recently, many attribute reduction algorithms, such as positive region preservation, generalized decision preservation, and distribution preservation, have been proposed. The existing attribute reduction algorithms for generalized decision preservation are mainly based on the discernibility matrix and are, thus, computationally very expensive and hard to use in large-scale and high-dimensional data sets. To overcome this problem, we introduce the similarity degree for generalized decision preservation. On this basis, the inner and outer significance measures are proposed. By using heuristic strategies, we develop two quick reduction algorithms for generalized decision preservation. Finally, theoretical and experimental results show that the proposed heuristic reduction algorithms are effective and efficient.

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“…But in recent years there have been more and more studies involving the decision rule acquisition. Papers [2,3] gave discernibility matrix or the discernibility function-based methods to acquire decision rules. These methods are able to acquire all minimum rule sets for a given decision system theoretically, but they usually would pay both huge time cost and huge space cost, which extremely narrow their applications in real life.…”

Section: Introductionmentioning

confidence: 99%

An Attribute-Value Block Based Method of Acquiring Minimum Rule Sets: A Granulation Method to Construct Classifier

Meng

Gan

2016

Intelligent Information Processing VIII

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Decision rule acquisition is one of the important topics in rough set theory and is drawing more and more attention. In this paper, decision logic language and attribute-value block technique are introduced first. And then realization methods of rule reduction and rule set minimum are relatively systematically studied by using attribute-value block technique, and as a result effective algorithms of reducing decision rules and minimizing rule sets are proposed, which, together with related attribute reduction algorithm, constitute an effective granulation method to acquire minimum rule sets, which is a kind classifier and can be used for class prediction. At last, related experiments are conducted to demonstrate that the proposed methods are effective and feasible.

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Attribute reduction and optimal decision rules acquisition for continuous valued information systems

Cited by 33 publications

References 48 publications

A Quick Algorithm for Binary Discernibility Matrix Simplification using Deterministic Finite Automata

A Quick Algorithm for Binary Discernibility Matrix Simplification using Deterministic Finite Automata

Heuristic Approaches to Attribute Reduction for Generalized Decision Preservation

An Attribute-Value Block Based Method of Acquiring Minimum Rule Sets: A Granulation Method to Construct Classifier

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