Attribute Reduction With Imputation Of Missing Data Using Fuzzy-Rougsh Set
Pallab Kumar Dey

Pallab Kumar Dey, Department Of Computer Science, Kalna College, Kalna-713409, India.
Manuscript received on 03 September 2019. | Revised Manuscript received on 22 September 2019. | Manuscript published on 30 September 2019. | PP: 202-207 | Volume-8 Issue-11, September 2019. | Retrieval Number: K12810981119/2019©BEIESP | DOI: 10.35940/ijitee.K1281.0981119
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: Attribute Reduction and missing data imputation have considerable influence in classification or other data mining task. New hybridization methodology like fuzzy rough set is more robust method to deal with imprecision and uncertainty for discrete as well as continuous data. Fuzzy rough attribute reduction with imputation (FRARI) algorithm has been proposed for attribute reduction with missing value imputation. So using FRARI algorithm complete reduce data set can be generated which has a great importance in different branches of artificial intelligence for data mining from databases. Efficiency and effectiveness of the proposed algorithm has been shown by experiment with real life data set.
Keywords: Attribute reduction, Data analysis, Fuzzy-rough set, Fuzzy set, Imputation, Missing value, Pre-processing, Rough set.
Scope of the Article: