Attribute Reduction Based on Genetic Algorithm for the Coevolution of Meteorological Data in the Industrial Internet of Things

Cheng, Yong; Zheng, Zhongren; Wang, Jun; Yang, Ling; Wan, Shaohua

doi:10.1155/2019/3525347

Cited by 15 publications

(3 citation statements)

References 38 publications

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“…The combination of intelligent algorithm and rough set theory for attribute reduction has great practical significance [17,18,19]. Minimum attribute reduction is to find the most appropriate subset of attributes in all attributes, which can also be understood as the problem of optimizing in all attributes.…”

Section: Introductionmentioning

confidence: 99%

Attribute Reduction Method Based on Binary Sparrow Search Algorithm and Neighborhood Rough Set

Liu

Chun

et al. 2022

Preprint

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In this paper, an attribute reduction algorithm of neighborhood rough set based on variable precision is proposed, and the lower approximate calculation formula is redefined, which solves the shortcomings of traditional neighborhood rough set, such as fault tolerance and weak noise resistance. Considering that the traditional attribute reduction algorithm is serial search, it is easy to fall into the local optimal solution. Therefore, this paper proposes to combine intelligent optimization algorithm with neighborhood rough set. On the basis of the excellent performance of continuous sparrow search algorithm (SSA), a binary sparrow algorithm (BSSA) is proposed to solve the binary discrete problem, which is used to solve the optimal combination of attributes. In this paper, according to the definition of positive region monotonicity, distance matrix is introduced. For the positive region sample in a single attribute, it can be omitted in the subsequent calculation, and the calculation of positive region is transformed into the form of adding several matrices, which provides an effective method to solve positive region for variable precision neighborhood rough set. Finally, five UCI standard data sets were used as experimental objects to compare with other 4 attribute reduction algorithms. The experimental results show that both the number of attribute parsimony and the accuracy of classification, the proposed algorithm shows a strong advantage.

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Section: Introductionmentioning

confidence: 99%

Attribute Reduction Method Based on Binary Sparrow Search Algorithm and Neighborhood Rough Set

Liu

Chun

et al. 2022

Preprint

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“…In recent years, some new attribute reduction algorithms are proposed. These methods are mainly based on information granularity [18], [19], the similarity degree [10], incremental mechanism [20], positive approximation [9], the dominance relation [12], [21], the graph theory [22], the Map Reduce [23], the information entropy theory [24], [25], [26], [27],the discernibility matrix [28], the uncertainty measure [29], the various intelligent optimization algorithm (such as genetic algorithm [30], particle swarm [31], ant colony [32], the binary bat algorithm [33], the artificial bee colony [34], [35]), and so on. Most of the algorithms are heuristic [8], [10], [18], [36], [37], [38], which is regarded as the best method to get the minimum reduction set [39].…”

Section: Introductionmentioning

confidence: 99%

Attribute Reduction Method Based on Generalized Grey Relational Analysis and Decision-Making Trial and Evaluation Laboratory

et al. 2020

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Attribute reduction is a challenging issue in intelligent manufacturing. Existing methods are mainly based on rough set theory (RST) focusing on symbolic and discrete values. However, classical RST doesn't consider the complex interrelationship among conditional attributes and consecutive attribute values. Our paper seeks to deal with this problem by proposing a hybrid framework based on generalized grey relational analysis (GGRA) and decision-making trial and evaluation laboratory (DEMATEL) method. GGRA is used to determine the initial importance of conditional attributes relative to the decision attribute and the direct relationship among conditional attributes. The causal relationships among conditional attributes are calculated by the DEMATEL method. Then we calculate the final importance of the conditional attribute relative to the decision attribute and apply a threshold to control the number of attributes entering the core set. Finally, an illustrative case and experiments are verified our method of attribute reduction with consecutive attribute values and complex interrelationship among attributes. INDEX TERMS attribute reduction; generalized grey relational analysis (GGRA); decision-making trial and evaluation laboratory (DEMATEL); attribute importance

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“…Dai et al [35] used a variant form of conditional entropy to design an attribute reduction algorithm for an interval-valued decision system. Many attribute reduction algorithms based on metaheuristic methods [24][25][26][27][28][29][30]36,37] have been developed recently. Chebrolu et al [26] used a genetic algorithm to obtain a global minimal reduct in a decision-theoretic rough set model.…”

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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Attribute Reduction Based on Genetic Algorithm for the Coevolution of Meteorological Data in the Industrial Internet of Things

Cited by 15 publications

References 38 publications

Attribute Reduction Method Based on Binary Sparrow Search Algorithm and Neighborhood Rough Set

Attribute Reduction Method Based on Binary Sparrow Search Algorithm and Neighborhood Rough Set

Attribute Reduction Method Based on Generalized Grey Relational Analysis and Decision-Making Trial and Evaluation Laboratory

Heuristic Approaches to Attribute Reduction for Generalized Decision Preservation

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