Rough Sets: An Overview, Hybridization and Applications
Amit Saxena1, Leeladhar Kumar Gavel2, Madan Madhaw Shrivas3
1Prof. Amit Saxena, Department of CSIT, Guru Ghasidas Vishwavidyalaya, Bilaspur, India.
2Leeladhar Kumar Gavel, Department of CSIT, Guru Ghasidas Vishwavidyalaya, Bilaspur, India.
3Madan Madhaw Shrivas, Department of CSIT, Guru Ghasidas Vishwavidyalaya, Bilaspur, India.
Manuscript received on January 12, 2015. | Revised Manuscript received on January 13, 2015. | Manuscript published on January 25, 2015. | PP:17-26 | Volume-3 Issue-3, January 2015. | Retrieval Number: C0896013315
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Rough set theory has emerged as a useful mathematical tool to extract conclusions or decisions from real life data involving vagueness, uncertainty and impreciseness and is therefore applied successfully in the field of pattern recognition, machine learning and data mining. This paper presents basic concepts and terms of rough set theory. The paper also presents hybridization approach of rough sets with various other established techniques along with developments from time to time.
Keywords: Pattern recognition, rough sets, hybridization of rough sets, neural networks, fuzzy sets.