Imputation of Missing Data using Fuzzy-Rough Hybridization - Dey, Pallab kumar (International Journal of Computer Sciences and Engineering, 2025)
Missing data imputation has a significant impact in data mining task. Data mining algorithms cannot be executed effectively due to missing attribute values. Improper handle of missing values affects the data mining and classification accuracy. Imputation based preprocessing approach is very effective technique for handling missing value. In this paper most similar object used to impute missing value. For searching similar object core attributes have to give highest priority after that reduct attributes. In the proposed method to fill missing value concept of core and reduct attributes has been used. Rough set is most suitable to handle discrete data. Fuzzy set can handle continuous data in a better way. Hybridization methodology like fuzzy-rough set are more powerful to deal with imprecision and uncertainty for discrete as well as continuous data. Detail study has been given to impute missing value. Fuzzy rough set based fuzz- rough core reduct based (FRCRB) algorithm has been proposed for missing value imputation