Lower Approximation Reduction Based on Discernibility Information Tree in Inconsistent Ordered Decision Information Systems

Zhang, Jia; Zhang, Xiaoyan; Xu, Weihua

doi:10.3390/sym10120696

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…The indiscernibility relation of the set of all condition attributes is called the equivalent class, which is then used to obtain a discernibility matrix [17], [18].…”

Section: Rough Set Algorithmmentioning

confidence: 99%

Classification of Toddler’s Nutritional Status Using the Rough Set Algorithm

Rahmi,

Wulandari,

Yozza

et al. 2023

BAREKENG: J. Math. & App.

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The health and nutrition of children at the age of five are very important aspects in the children’s growth and development. An assessment of the nutritional status of toddlers that is commonly used is anthropometry. This study aims to obtain the decision rules used to classify toddlers into nutritional status groups using the rough set algorithm and determine the level of classification accuracy of the resulting decision rules. The index used in this study is the weight-for-age index. Attributes used in this study were the mother’s education level, mother’s level of knowledge, the status of exclusive breastfeeding, history of illness in the last month, and nutritional status of toddlers. The results of the analysis show that there are 21 decision rules. In this study, the resulting decision rules experience inconsistencies. The selection of decision rules that experience inconsistencies is based on each decision rule’s highest strength value. The rough set algorithm can be used for the classification process with an accuracy rate of 86.36%.

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“…The indiscernibility relation of the set of all condition attributes is called the equivalent class, which is then used to obtain a discernibility matrix [17], [18].…”

Section: Rough Set Algorithmmentioning

confidence: 99%

Classification of Toddler’s Nutritional Status Using the Rough Set Algorithm

Rahmi,

Wulandari,

Yozza

et al. 2023

BAREKENG: J. Math. & App.

View full text Add to dashboard Cite

show abstract

“…At the same time, many formulas or methods were proposed to calculate the different types of attribute significances. Some classical formulas are designed based on the positive region [28][29][30], entropy [3,[16][17][18], the discernibility ability of attributes [13,14,24,31,32], the relationship between attributes [33], etc. In addition, many researchers proposed the mixed formulas by combining rough set theory and other theories, such as fuzzy set [12], ant colony optimization [23], granular computing [2,6,16,34], etc.…”

Section: Introductionmentioning

confidence: 99%

A Fast Attribute Reduction Algorithm Based on a Positive Region Sort Ascending Decision Table

Liang

Jiang

2020

Symmetry

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Attribute reduction is one of the challenging problems in rough set theory. To accomplish an efficient reduction algorithm, this paper analyzes the shortcomings of the traditional methods based on attribute significance, and suggests a novel reduction way where the traditional attribute significance calculation is replaced by a special core attribute calculation. A decision table called the positive region sort ascending decision table (PR-SADT) is defined to optimize some key steps of the novel reduction method, including the special core attribute calculation, positive region calculation, etc. On this basis, a fast reduction algorithm is presented to obtain a complete positive region reduct. Experimental tests demonstrate that the novel reduction algorithm achieves obviously high computational efficiency.

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“…Considering the ensemble learning problem, Wang et al [17] introduced the forest optimization algorithm into the process of picking up reduct which can return multiple reducts, and used these reducts to develop an ensemble framework for executing voting classification over testing samples. Considering the monotonic classification problem, Zhang et al [18] applied the matrix approach for lower approximation in an inconsistent decision system to give the discriminative con-cept tree with the relations by dominance, and then fused the evaluation functions by tree approach to establish an efficient algorithm of searching lower approximation reduct. Considering the semi-supervised learning problem, Liu et al [19] proposed a semi-supervised attribute reduction approach that can handle the partially labeled data with label propagation algorithm and ensemble selector.…”

mentioning

confidence: 99%

Effective Attribute Reduction Algorithm Based on Fuzzy Uncertainties Using Shared Neighborhood Granulation

Gao

2024

IEEE Access

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As a very prominent research application of the theory of rough sets, attribute reduction technique has made significant strides in a lot of fields, including decision making, granular computing, etc. In particular, fuzzy attribute reduction approaches contribute greatly in the presence of uncertain data. However, most of fuzzy relations used in these approaches lack the discriminant ability to sample similarity, failing to identify the feature significance satisfactorily. In this article, a novel scheme using the shared neighborhood fuzzy uncertainties is proposed. Firstly, the concept of shared neighborhood is formulated, and then employed to establish the fuzzy similarity relation that effectively captures the sample similarity. Secondly, two fuzzy uncertainty measures named joint entropy and discrimination index based on shared neighborhood fuzzy relation are defined, which can quantify the feature's significance to the uncertainty characterization. Finally, two heuristic searching algorithms are designed to identify reducts aimed at minimizing the fuzzy uncertainties. Some comparative studies are investigated to examine the advantage of the designed reduction algorithms in classifier modeling. The reported analyses on public data sets verify that the designed algorithms outperform some representative and latest algorithms.

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Lower Approximation Reduction Based on Discernibility Information Tree in Inconsistent Ordered Decision Information Systems

Cited by 4 publications

References 34 publications

Classification of Toddler’s Nutritional Status Using the Rough Set Algorithm

Classification of Toddler’s Nutritional Status Using the Rough Set Algorithm

A Fast Attribute Reduction Algorithm Based on a Positive Region Sort Ascending Decision Table

Effective Attribute Reduction Algorithm Based on Fuzzy Uncertainties Using Shared Neighborhood Granulation

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