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Rough Set Based Rule Approximation and Application on Uncertain Datasets



Development of new Artificial Intelligence related data analysis methodologies with revolutionary information technology has made a radical change in prediction, forecasting, and decision making for real-world data. The challenge arises when the real world dataset consisting of voluminous data is uncertain. The rough set is a mathematical formalism that has emerged significantly for uncertain datasets. It represents the knowledge of the datasets as decision rules. It does not need any metadata. The rules are used to predict or classify unseen examples. The objective of this research is to develop a rough set based classification system that predicts and classifies unseen examples by learning from the minimal subset of decision rules extracted from uncertain datasets using rule approximation. This paper proposes a novel rule approximation classifier, Weighted-Attribute Significance Rule Approximation (WASRA) that uses a subset of the decision rules generated by any rule induction algorithm, to compute the concept weights of the condition attributes. The concept weights and the significance of condition attributes are used to design a novel classifier. This classifier is implemented and initially tested on a few benchmarked datasets of the UCI repository. The classifier is subsequently tested on a real-time dataset and compared to other standard classifiers. The experimental results illustrate that the proposed WASRA performs well and shows an improvement in the prediction accuracy compared to other classifiers. This classifier can be applied to any dataset which has uncertainty.



Total Pages: 14


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Online Article


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


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