A Decision Analysis Method Based on Rough Set

Chen, Shenghai; Jiang, Zongyan; Duan, Chao; Dai, Xuejing; Mi, Xueling

doi:10.2991/tlicsc-18.2018.8

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…To describe the property of the rule set in general, evaluation metrics of the rules set can be used. The single-rule evaluation metric measures of an individual rule (Chen et al [2]). Threeway decisions, i.e., positive, boundary, and negative rules, are offered as a different approach to interpreting rules in rough set theory.…”

Section: Related Workmentioning

confidence: 99%

Rough Set Based Decision Support for Feature Extraction of Rice Data

Saxena,

Sharma,

Panchal

2023

Comm. Math. Appl.

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The use of technology in the agriculture sector makes it more productive. Different technologies are used for quality control, classification, and prediction of grains in agriculture. It is challenging for decision-making with big agricultural data to concentrate more when many features are given in the data. It is difficult for users to scrutinize the market's excellent quality rice. Determining rice quality by the visual judgment of human inspectors is neither practical nor reliable. Therefore, a proven methodology is essential for the rice quality classifying system, which will overcome the manual quality classification process. In this study, the concept of Rough Set Theory is applied to find a set of minimal attributes and generate a set of decision rules for predicting rice type.

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Section: Related Workmentioning

confidence: 99%

Rough Set Based Decision Support for Feature Extraction of Rice Data

Saxena,

Sharma,

Panchal

2023

Comm. Math. Appl.

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“…Decision rules are statements of the form "if f then g" represented as 𝑓 → 𝑔. f part is the value of the condition attribute and 𝑔 part is the value of the decision attribute. In the rough set, decision rules can be drawn from the resulting reduct by observing the table of equivalent classes formed [20].…”

Section: F Formation Of Decision Rulesmentioning

confidence: 99%

“…To overcome these problems, a quality measure is used to select decision rules that experience inconsistencies. Quality measures are divided into support, strength, accuracy, and coverage [20]. In this study, strength is used as a reference to determine the decision to be used.…”

Section: F Formation Of Decision Rulesmentioning

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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A Decision Analysis Method Based on Rough Set

Cited by 2 publications

References 5 publications

Rough Set Based Decision Support for Feature Extraction of Rice Data

Rough Set Based Decision Support for Feature Extraction of Rice Data

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

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