Rough Sets for Feature Selection and Classification: An Overview with Applications
Amit Saxena1, Leeladhar Kumar Gavel2, Madan Madhaw Shrivas3
1Amit Saxena, Department of CSIT, Guru Ghasidas Vishwavidyalaya, Bilaspur, (Chhattisgarh), India.
2Leeladhar Kumar Gavel, Department of CSIT, Guru Ghasidas Vishwavidyalaya, Bilaspur, (Chhattisgarh), India.
3Madan Madhaw Shrivas, Department of CSIT, Guru Ghasidas Vishwavidyalaya, Bilaspur, (Chhattisgarh), India.
Manuscript received on 20 November 2014 | Revised Manuscript received on 30 November 2015 | Manuscript published on 30 November 2014 | PP: 62-69 | Volume-3 Issue-5, November 2014 | Retrieval Number: E1269113514/2014©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. 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 provides a useful mathematical concept to draw useful decisions from real life data involving vagueness, uncertainty and impreciseness and is therefore applied successfully in the field of pattern recognition, machine learning and knowledge discovery. This paper presents an overview of basic concepts of rough set theory. The paper also surveys applications of rough sets in feature selection and classification.
Keywords: Pattern recognition, Feature selection, Classification.
Scope of the Article: Classification