Abstract
Decision making has become a primary motive of data analytics in the present scenario. Prior to analysis data has to be set free from noise by applying data preprocessing techniques to the raw data. Missing value imputation is one of the data cleaning method in data preprocessing. This article presents a novel data imputation technique with the concepts of rough set theory. An imputation algorithm Rough Set Missing Value Imputation (RSMVI) is developed. The performance of the proposed algorithm is carried out by comparing the classification accuracy obtained, after the missing value imputation is performed. C4.5 classifier is chosen for the same. Cleveland heart data set has been used for evaluation of the proposed algorithm.
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Sujatha, M., Lavanya Devi, G., Srinivasa Rao, K., Ramesh, N. (2018). Rough Set Theory Based Missing Value Imputation. In: Cognitive Science and Health Bioinformatics. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-10-6653-5_9
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DOI: https://doi.org/10.1007/978-981-10-6653-5_9
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