A rough set approach aim to space weather and solar storms prediction

Authors:
Reza Mahini;Caro Lucas;Masoud Mirmomeni;Hassan Rezazadeh
Affiliations:
Electrical, Computer and IT Eng. Department, Payame Noor University of Tabriz, Iran;Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Iran;Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Iran and Departmentment of Computer Science and Engineering, Michigan ...;Industrial Engineering Department, University of Tabriz, Iran
Venue:
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part I
Year:
2011

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Abstract

This paper illustrates using Rough set theory as a data mining method for modeling Alert systems. A data-driven approach is applied to design a reliable alert system for prediction of different situations and setting off of the alerts for various critical parts of human industry sections. In this system preprocessing and reduction of data with data mining methods is performed. Rough set learning method is used to attain the regular and reduced knowledge from the system behaviors. Finally, using the produced and reduced rules extracted from rough set reduction algorithms, the obtained knowledge is applied to reach this purpose. This method, as demonstrated with successful realistic applications, makes the present approach effective in handling real world problems. Our experiments indicate that the proposed model can handle different groups of uncertainties and impreciseness accuracy and get a suitable predictive performance when we have several certain features set for representing the knowledge.