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The imputation of unknown or missing data is a crucial task on the analysis of biomedical datasets. There are several situations where it is necessary to classify or identify instances given incomplete vectors, and the existence of missing values can much degrade the performance of the algorithms used for the classification/recognition. The task of learning accurately from incomplete data raises a...
Unknown data is a common drawback in medical diagnosis applications. A recommended procedure for dealing with unknown values is missing data imputation, i.e., estimating and filling missing values using all the available information. This work* presents a robust approach for incomplete data classification using an enhanced version of the K Nearest Neighbours algorithm. Experimental results on medical...
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