Open Access

ARTICLE

Rough Set Based Rule Approximation and Application on Uncertain Datasets

by L. Ezhilarasi, A.P. Shanthi, V. Uma Maheswari

1 Department of Information Science and Technology, College of Engineering, Guindy, Anna University, Chennai, Tamil Nadu,600025, India
2 Department of Computer Science and Engineering, College of Engineering, Guindy, Anna University, Chennai, Tamil Nadu, 600025, India.

* Corresponding Author: L.Ezhilarasi,

Intelligent Automation & Soft Computing 2020, 26(3), 465-478. https://doi.org/10.32604/iasc.2020.013923

Abstract

Development of new Artificial Intelligence related data analy sis methodologies w ith rev olutionary information technology has made a radical change in prediction, forecasting, and decision making for real-w orld data. The challenge arises w hen the real w orld dataset consisting of v oluminous data is uncertain. The rough set is a mathematical formalism that has emerged significantly for uncertain datasets. It represents the know ledge of the datasets as decision rules. It does not need any metadata. The rules are used to predict or classify unseen ex amples. The objectiv e of this research is to dev elop a rough set based classification sy stem that predicts and classifies unseen ex amples by learning from the minimal subset of decision rules ex tracted from uncertain datasets using rule approx imation. This paper proposes a nov el rule approx imation classifier, Weighted-Attribute Significance Rule Approx imation (WASRA) that uses a subset of the decision rules generated by any rule induction algorithm, to compute the concept w eights of the condition attributes. The concept w eights and the significance of condition attributes are used to design a nov el 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 ex perimental results illustrate that the proposed WASRA performs w ell and show s an improv ement in the prediction accuracy compared to other classifiers. This classifier can be applied to any dataset w hich has uncertainty .

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Keywords

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