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Decision making from data involves identifying a set of attributes thatcontribute to effective decision making through computational intelligence. Thepresence of missing values greatly influences the selection of right set ofattributes and this renders degradation in classification accuracies of theclassifiers. As missing values are quite common in data collection phase duringfield experiments or clinical trails appropriate handling would improve theclassifier performance. In this paper we present a review of recently developedmissing value imputation algorithms and compare their performance on some benchmark datasets.

Performance comparison of State-of-the-art Missing Value Imputation Algorithms on Some Bench mark Datasets