An accelerator for attribute reduction based on perspective of objects and attributes

Liang, Jiye; Mi, Junrong; Wei, Wei; Wang, Rui

doi:10.1016/j.knosys.2013.01.027

Cited by 37 publications

(10 citation statements)

References 38 publications

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“…According to (18), we can select the attribute with largest SGF. If the cost of the decision table after adding the attribute decreases, it indicates that the attribute can help reduce the cost.…”

Section: Minimum Cost Attribute Reduction Based On Mutual Informationmentioning

confidence: 99%

“…The objective of attribute reduction is to remove the redundant attributes and, at the same time, to preserve the classification ability of the original attributes. It has drawn the attention of many researchers [12,15,[18][19][20][21][22]. Different attribute reduction algorithms in rough set theory have been proposed for achieving reducts [20,[23][24][25][26][27][28] and have wide applications 4126 Z. BI ET AL.…”

Section: Introductionmentioning

confidence: 99%

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Attribute reduction in decision‐theoretic rough set model based on minimum decision cost

Lei

et al. 2016

Concurrency and Computation

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SUMMARYAttribute reduction is one of the most important topics in rough set theory. In the classical rough sets, the method for attribute reduction is mainly to keep positive region, boundary region, and negative region unchanged. However, the three regions are no longer monotonic with respect to adding or deleting an attribute in decision-theoretic rough sets. In decision-theoretic rough set model, the decision regions are determined by using the Bayesian decision procedure, and decision-making should take consideration of the cost. In this paper, two attribute reduction methods based on minimum decision cost are proposed from the algebraic view and the information theory, respectively. First, significance of joint attributes is introduced to measure the classification ability of selected attribute subset to decision-making, which overcomes the disadvantage of only considering the significance of single attribute. By using significance of joint attributes, a heuristic method based on minimum decision cost for attribute reduction is presented. Second, conditional mutual information is proposed to evaluate the significance of attribute subset for decision-making in minimum cost attribute reduction. To decrease the computational complexity of the conditional mutual information, an approximate computation method is calculated from both maximum relevance and maximum significance. To evaluate the two proposed algorithms, extensive experiments are conducted on 10 University of California at Irvine data sets. We compare our proposed algorithms with several existing cost minimization attribute reduction algorithms. Experiment results show that our proposed algorithms have a superior performance in achieving the reduct.

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Section: Minimum Cost Attribute Reduction Based On Mutual Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Attribute reduction in decision‐theoretic rough set model based on minimum decision cost

Lei

et al. 2016

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…The heuristic reduction methods based on the accelerator can significantly decrease the consuming time and obtain the same reduct as their original methods. To further enhance the efficiency of reduction algorithms, Liang et al (2013) developed a new accelerator for attribute reduction, which simultaneously reduced the size of the universe and the number of attributes at the each iteration of the process of reduction. Liang et al (2012) divided the big data set into different small sub-data sets called as granularities, and fused the feature selection results of small granularities together to find an approximation reduct.…”

Section: Related Workmentioning

confidence: 99%

Bidirectional heuristic attribute reduction based on conflict region

Long-shu

et al. 2014

Soft Comput

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Attribute reduction is one of the key issues in rough set theory. Many heuristic reduction strategies such as forward heuristic reduction, backward heuristic reduction and for-backward heuristic reduction have been proposed to obtain a subset of attributes which has the same discernibility as the original attribute set. However, some methods are usually computationally time consuming for large data sets. Therefore, this paper focuses on solving the attribute reduction efficiency in the decision system. We first introduce the quotient of approximation, positive region and conflict region, and then research the heuristic reduction algorithm based on conflict region. Sequentially, we put forward to a mechanism of bidirectional heuristic attribute reduction based on conflict region quotient and design a bidirectional heuristic attribute reduction algorithm. Finally, the experimental results with UCI data sets show that the proposed reduction algorithm is an effective technique to deal with large high-dimensional data sets.

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“…Being an important issue in knowledge discovery, attribute reduction has been extensively studied in different fields of soft computing since it can decrease the dimension and make the data easily be understood [26][27][28][29][30][31][32][33][34][35] . It is also an interesting topic in FCA.…”

Section: Introductionmentioning

confidence: 99%

The attribute reductions of three-way concept lattices

Ren

Ling

2016

Knowledge-Based Systems

121

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Object-induced three-way object concept Attribute-induced three-way attribute concept Discernibility attribute set a b s t r a c t Three-way concept analysis is a newly proposed area of formal concept analysis from which one can obtain both the inclusion decision and the exclusion decision. In general, given a context, some attributes may not be essential in three-way concept analysis, such as forming three-way concept lattice. So in this paper, we study the attribute reductions of three-way concept lattices in order to make the data easily be understood. Firstly, based on different criteria generated from object-induced three-way concept (OEconcept), four kinds of attribute reductions are proposed. The four reductions together embody different characteristics of a formal context and can be used in different occasions. Secondly, we discuss their relationships, including their advantages and disadvantages and the relationships among consistent sets and among the cores. Thirdly, based on attribute-induced three-way concept (AE-concept), we also give four attribute-induced three-way attribute reductions and discuss their relationships. Finally, the approaches to computing these attribute reductions are presented and the obtained results are demonstrated and verified by an empirical case. In this paper, we systematically investigate the attribute reductions of threeway concept lattices which enriches the study of formal concept analysis.

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An accelerator for attribute reduction based on perspective of objects and attributes

Cited by 37 publications

References 38 publications

Attribute reduction in decision‐theoretic rough set model based on minimum decision cost

Attribute reduction in decision‐theoretic rough set model based on minimum decision cost

Bidirectional heuristic attribute reduction based on conflict region

The attribute reductions of three-way concept lattices

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